steps of thematic analysis in qualitative research

How to do thematic analysis

Last updated

8 February 2023

Reviewed by

Miroslav Damyanov

Uncovering themes in data requires a systematic approach. Thematic analysis organizes data so you can easily recognize the context.

• What is thematic analysis?

Thematic analysis is   a method for analyzing qualitative data that involves reading through a data set and looking for patterns to derive themes . The researcher's subjective experience plays a central role in finding meaning within the data.

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• What are the main approaches to thematic analysis?

Inductive thematic analysis approach

Inductive thematic analysis entails   deriving meaning and identifying themes from data with no preconceptions.  You analyze the data without any expected outcomes.

Deductive thematic analysis approach

In the deductive approach, you analyze data with a set of expected themes. Prior knowledge, research, or existing theory informs this approach.

Semantic thematic analysis approach

With the semantic approach, you ignore the underlying meaning of data. You take identifying themes at face value based on what is written or explicitly stated.

Latent thematic analysis approach

Unlike the semantic approach, the latent approach focuses on underlying meanings in data and looks at the reasons for semantic content. It involves an element of interpretation where you theorize meanings and don’t just take data at face value.

• When should thematic analysis be used?

Thematic analysis is beneficial when you’re working with large bodies of data. It allows you to divide and categorize huge quantities of data in a way that makes it far easier to digest.  

The following scenarios warrant the use of thematic analysis:

You’re new to qualitative analysis

You need to identify patterns in data

You want to involve participants in the process

Thematic analysis is particularly useful when you’re looking for subjective information such as experiences and opinions in surveys , interviews, conversations, or social media posts. 

• What are the advantages and disadvantages of thematic analysis?

Thematic analysis is a highly flexible approach to qualitative data analysis that you can modify to meet the needs of many studies. It enables you to generate new insights and concepts from data. 

Beginner researchers who are just learning how to analyze data will find thematic analysis very accessible. It’s easy for most people to grasp and can be relatively quick to learn.

The flexibility of thematic analysis can also be a disadvantage. It can feel intimidating to decide what’s important to emphasize, as there are many ways to interpret meaning from a data set.

• What is the step-by-step process for thematic analysis?

The basic thematic analysis process requires recognizing codes and themes within a data set. A code is a label assigned to a piece of data that you use to identify and summarize important concepts within a data set. A theme is a pattern that you identify within the data. Relevant steps may vary based on the approach and type of thematic analysis, but these are the general steps you’d take:

1. Familiarize yourself with the data(pre-coding work)

Before you can successfully work with data, you need to understand it. Get a feel for the data to see what general themes pop up. Transcribe audio files and observe any meanings and patterns across the data set. Read through the transcript, and jot down notes about potential codes to create. 

2. Create the initial codes (open code work)

Create a set of initial codes to represent the patterns and meanings in the data. Make a codebook to keep track of the codes. Read through the data again to identify interesting excerpts and apply the appropriate codes. You should use the same code to represent excerpts with the same meaning. 

3. Collate codes with supporting data (clustering of initial code)

Now it's time to group all excerpts associated with a particular code. If you’re doing this manually, cut out codes and put them together. Thematic analysis software will automatically collate them.

4. Group codes into themes (clustering of selective codes)

Once you’ve finalized the codes, you can sort them into potential themes. Themes reflect trends and patterns in data. You can combine some codes to create sub-themes.

5. Review, revise, and finalize the themes (final revision)

Now you’ve decided upon the initial themes, you can review and adjust them as needed. Each theme should be distinct, with enough data to support it. You can merge similar themes and remove those lacking sufficient supportive data. Begin formulating themes into a narrative. 

6. Write the report

The final step of telling the story of a set of data is writing the report. You should fully consider the themes to communicate the validity of your analysis.

A typical thematic analysis report contains the following:

An introduction

A methodology section

Results and findings

A conclusion

Your narrative must be coherent, and it should include vivid quotes that can back up points. It should also include an interpretive analysis and argument for your claims. In addition, consider reporting your findings in a flowchart or tree diagram, which can be independent of or part of your report.  

In conclusion, a thematic analysis is a method of analyzing qualitative data. By following the six steps, you will identify common themes from a large set of texts. This method can help you find rich and useful insights about people’s experiences, behaviors, and nuanced opinions.

• How to analyze qualitative data

Qualitative data analysis is the process of organizing, analyzing, and interpreting non-numerical and subjective data . The goal is to capture themes and patterns, answer questions, and identify the best actions to take based on that data. 

Researchers can use qualitative data to understand people’s thoughts, feelings, and attitudes. For example, qualitative researchers can help business owners draw reliable conclusions about customers’ opinions and discover areas that need improvement. 

In addition to thematic analysis, you can analyze qualitative data using the following:

Content analysis

Content analysis examines and counts the presence of certain words, subjects, and contexts in documents and communication artifacts, such as: 

Text in various formats

This method transforms qualitative input into quantitative data. You can do it manually or with electronic tools that recognize patterns to make connections between concepts.  

Narrative analysis

Narrative analysis interprets research participants' stories from testimonials, case studies, interviews, and other text or visual data. It provides valuable insights into the complexity of people's feelings, beliefs, and behaviors.

Discourse analysis

In discourse analysis , you analyze the underlying meaning of qualitative data in a particular context, including: 

Historical 

This approach allows us to study how people use language in text, audio, and video to unravel social issues, power dynamics, or inequalities. 

For example, you can look at how people communicate with their coworkers versus their bosses. Discourse analysis goes beyond the literal meaning of words to examine social reality.

Grounded theory analysis

In grounded theory analysis, you develop theories by examining real-world data. The process involves creating hypotheses and theories by systematically collecting and evaluating this data. While this approach is helpful for studying lesser-known phenomena, it might be overwhelming for a novice researcher. 

• Challenges with analyzing qualitative data

While qualitative data can answer questions that quantitative data can't, it still comes with challenges.

If done manually, qualitative data analysis is very time-consuming.

It can be hard to choose a method. 

Avoiding bias is difficult.

Human error affects accuracy and consistency.

To overcome these challenges, you should fine-tune your methods by using the appropriate tools in collaboration with teammates.

Learn more about thematic analysis software

What is thematic analysis in qualitative research.

Thematic analysis is a method of analyzing qualitative data. It is applied to texts, such as interviews or transcripts. The researcher closely examines the data to identify common patterns and themes.

Can thematic analysis be done manually?

You can do thematic analysis manually, but it is very time-consuming without the help of software.

What are the two types of thematic analysis?

The two main types of thematic analysis include codebook thematic analysis and reflexive thematic analysis.

Codebook thematic analysis uses predetermined codes and structured codebooks to analyze from a deductive perspective. You draw codes from a review of the data or an initial analysis to produce the codebooks.

Reflexive thematic analysis is more flexible and does not use a codebook. Researchers can change, remove, and add codes as they work through the data. 

What makes a good thematic analysis?

The goal of thematic analysis is more than simply summarizing data; it's about identifying important themes. Good thematic analysis interprets, makes sense of data, and explains it. It produces trustworthy and insightful findings that are easy to understand and apply. 

What are examples of themes in thematic analysis?

Grouping codes into themes summarize sections of data in a useful way to answer research questions and achieve objectives. A theme identifies an area of data and tells the reader something about it. A good theme can sit alone without requiring descriptive text beneath it.

For example, if you were analyzing data on wildlife, codes might be owls, hawks, and falcons. These codes might fall beneath the theme of birds of prey. If your data were about the latest trends for teenage girls, codes such as mini skirts, leggings, and distressed jeans would fall under fashion.  

Thematic analysis is straightforward and intuitive enough that most people have no trouble applying it.

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How to do a thematic analysis

What is a thematic analysis?

When is thematic analysis used, braun and clarke’s reflexive thematic analysis, the six steps of thematic analysis, 1. familiarizing, 2. generating initial codes, 3. generating themes, 4. reviewing themes, 5. defining and naming themes, 6. creating the report, the advantages and disadvantages of thematic analysis, disadvantages, frequently asked questions about thematic analysis, related articles.

Thematic analysis is a broad term that describes an approach to analyzing qualitative data . This approach can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. Learn more about different research methods.

A researcher performing a thematic analysis will study a set of data to pinpoint repeating patterns, or themes, in the topics and ideas that are expressed in the texts.

In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics. This requires an approach to data that is complex and exploratory and can be anchored by different philosophical and conceptual foundations.

A six-step system was developed to help establish clarity and rigor around this process, and it is this system that is most commonly used when conducting a thematic analysis. The six steps are:

• Familiarization
• Generating codes
• Generating themes
• Reviewing themes
• Defining and naming themes
• Creating the report

It is important to note that even though the six steps are listed in sequence, thematic analysis is not necessarily a linear process that advances forward in a one-way, predictable fashion from step one through step six. Rather, it involves a more fluid shifting back and forth between the phases, adjusting to accommodate new insights when they arise.

And arriving at insight is a key goal of this approach. A good thematic analysis doesn’t just seek to present or summarize data. It interprets and makes a statement about it; it extracts meaning from the data.

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge.

Some examples of research questions that thematic analysis can be used to answer are:

• What are senior citizens’ experiences of long-term care homes?
• How do women view social media sites as a tool for professional networking?
• How do non-religious people perceive the role of the church in a society?
• What are financial analysts’ ideas and opinions about cryptocurrency?

To begin answering these questions, you would need to gather data from participants who can provide relevant responses. Once you have the data, you would then analyze and interpret it.

Because you’re dealing with personal views and opinions, there is a lot of room for flexibility in terms of how you interpret the data. In this way, thematic analysis is systematic but not purely scientific.

A landmark 2006 paper by Victoria Braun and Victoria Clarke (“ Using thematic analysis in psychology ”) established parameters around thematic analysis—what it is and how to go about it in a systematic way—which had until then been widely used but poorly defined.

Since then, their work has been updated, with the name being revised, notably, to “reflexive thematic analysis.”

One common misconception that Braun and Clarke have taken pains to clarify about their work is that they do not believe that themes “emerge” from the data. To think otherwise is problematic since this suggests that meaning is somehow inherent to the data and that a researcher is merely an objective medium who identifies that meaning.

Conversely, Braun and Clarke view analysis as an interactive process in which the researcher is an active participant in constructing meaning, rather than simply identifying it.

The six stages they presented in their paper are still the benchmark for conducting a thematic analysis. They are presented below.

This step is where you take a broad, high-level view of your data, looking at it as a whole and taking note of your first impressions.

This typically involves reading through written survey responses and other texts, transcribing audio, and recording any patterns that you notice. It’s important to read through and revisit the data in its entirety several times during this stage so that you develop a thorough grasp of all your data.

After familiarizing yourself with your data, the next step is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

In our example scenario, we’re researching the experiences of women over the age of 50 on professional networking social media sites. Interviews were conducted to gather data, with the following excerpt from one interview.

In the example interview snippet, portions have been highlighted and coded. The codes describe the idea or perception described in the text.

It pays to be exhaustive and thorough at this stage. Good practice involves scrutinizing the data several times, since new information and insight may become apparent upon further review that didn’t jump out at first glance. Multiple rounds of analysis also allow for the generation of more new codes.

Once the text is thoroughly reviewed, it’s time to collate the data into groups according to their code.

Now that we’ve created our codes, we can examine them, identify patterns within them, and begin generating themes.

Keep in mind that themes are more encompassing than codes. In general, you’ll be bundling multiple codes into a single theme.

To draw on the example we used above about women and networking through social media, codes could be combined into themes in the following way:

You’ll also be curating your codes and may elect to discard some on the basis that they are too broad or not directly relevant. You may also choose to redefine some of your codes as themes and integrate other codes into them. It all depends on the purpose and goal of your research.

This is the stage where we check that the themes we’ve generated accurately and relevantly represent the data they are based on. Once again, it’s beneficial to take a thorough, back-and-forth approach that includes review, assessment, comparison, and inquiry. The following questions can support the review:

• Has anything been overlooked?
• Are the themes definitively supported by the data?
• Is there any room for improvement?

With your final list of themes in hand, the next step is to name and define them.

In defining them, we want to nail down the meaning of each theme and, importantly, how it allows us to make sense of the data.

Once you have your themes defined, you’ll need to apply a concise and straightforward name to each one.

In our example, our “perceived lack of skills” may be adjusted to reflect that the texts expressed uncertainty about skills rather than the definitive absence of them. In this case, a more apt name for the theme might be “questions about competence.”

To finish the process, we put our findings down in writing. As with all scholarly writing, a thematic analysis should open with an introduction section that explains the research question and approach.

This is followed by a statement about the methodology that includes how data was collected and how the thematic analysis was performed.

Each theme is addressed in detail in the results section, with attention paid to the frequency and presence of the themes in the data, as well as what they mean, and with examples from the data included as supporting evidence.

The conclusion section describes how the analysis answers the research question and summarizes the key points.

In our example, the conclusion may assert that it is common for women over the age of 50 to have negative experiences on professional networking sites, and that these are often tied to interactions with other users and a sense that using these sites requires specialized skills.

Thematic analysis is useful for analyzing large data sets, and it allows a lot of flexibility in terms of designing theoretical and research frameworks. Moreover, it supports the generation and interpretation of themes that are backed by data.

There are times when thematic analysis is not the best approach to take because it can be highly subjective, and, in seeking to identify broad patterns, it can overlook nuance in the data.

What’s more, researchers must be judicious about reflecting on how their own position and perspective bears on their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

Thematic analysis offers a flexible and recursive way to approach qualitative data that has the potential to yield valuable insights about people’s opinions, views, and lived experience. It must be applied, however, in a conscientious fashion so as not to allow subjectivity to taint or obscure the results.

The purpose of thematic analysis is to find repeating patterns, or themes, in qualitative data. Thematic analysis can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics.

A big advantage of thematic analysis is that it allows a lot of flexibility in terms of designing theoretical and research frameworks. It also supports the generation and interpretation of themes that are backed by data.

A disadvantage of thematic analysis is that it can be highly subjective and can overlook nuance in the data. Also, researchers must be aware of how their own position and perspective influences their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

How many themes make sense in your thematic analysis of course depends on your topic and the material you are working with. In general, it makes sense to have no more than 6-10 broader themes, instead of having many really detailed ones. You can then identify further nuances and differences under each theme when you are diving deeper into the topic.

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge. Therefore, it makes sense to use thematic analysis for interviews.

After familiarizing yourself with your data, the first step of a thematic analysis is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Survey data and responses
• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis

Content analysis

• Introduction

What is meant by thematic analysis?

The thematic analysis process, thematic analysis in other research methods, using atlas.ti for qualitative analysis, considerations for thematic analysis.

• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research

Discourse analysis

Grounded theory.

• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative analysis software

Thematic analysis

One of the most straightforward forms of qualitative data analysis involves the identification of themes and patterns that appear in otherwise unstructured qualitative data . Thematic analysis is an integral component of qualitative research because it provides an entry point into analyzing qualitative data.

Let's look at thematic analysis, its role in qualitative research methods , and how ATLAS.ti can help you form themes from raw data to generate a theoretical framework .

The main objective of research is to order data into meaningful patterns and generate new knowledge arising from theories about that data. Quantitative data is analyzed to measure a phenomenon's quantifiable aspects (e.g., an element's melting point, the effective income tax rate in the suburbs). The advantage of quantitative research is that data is often already structured, or at least easily structured, to quickly draw insights from numerical values.

On the other hand, some phenomena cannot be easily quantified, or they require conceptual development before they can be quantified. For example, what do people mean when they think of a movie or TV show as "good"? In the everyday world, people in a casual discussion may judge the quality of entertainment as a matter of personal preference, something that cannot be defined, let alone universally understood.

As a result, researchers analyze qualitative data for identifying themes or phenomena that occur often or in telling patterns. In the case of TV shows, a collection of reviews of TV shows may frequently mention the acting, the script writing, and the production values, among other things. If these aspects are mentioned the most often, researchers can think of these as the themes determining the quality of a given TV show.

A useful metaphor for thematic analysis

Even if this is an easy concept to grasp, realizing this concept in qualitative research is a significant challenge. The biggest consideration for thematic analysis is that qualitative data is often unstructured and requires some organization to make it relevant to researchers and their audience.

Imagine that you have a bag of marbles. Each marble has one of a set of different colors. If you were to sort the marbles by color, you could determine how many colors are in the bag and which colors are the most common.

The thematic analysis process is similar to sorting different-colored marbles. Instead of sorting colors, you are sorting themes in a data set to determine which themes appear the most often or to identify patterns among these themes.

After your initial analysis, you can take this one step further and separate "dark" colors from "light" colors or "warm" colors from "cool" colors. Blue and green are distinctly different colors, but you can group them under the "cool" category of colors to form a more overarching theme.

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A simple example of thematic analysis

Imagine a simple research question : how do teachers determine if a student's essay is good? Suppose you have a set of transcripts of interviews with teachers discussing writing classes and students' essays. In this case, the objective of thematic analysis is to determine the main factors teachers use to determine the quality of a piece of writing.

As you read the transcripts, you might find that teachers share some common answers. Of course, you might have an intuition that correct grammar and spelling are important, which will likely be confirmed by the teachers in their interviews. However, other considerations might surface in the data.

The next question in this casual thematic analysis is, what considerations appear most often? A few teachers may occasionally mention the size and typeface of the text as deciding factors, but more often they might say that the flow and organization of students' writing are more important. Analyzing the occurrences and patterns among themes across your transcripts can help you develop an answer to your research question.

The subjectivity of themes

One challenge is that themes in qualitative analysis, as with determining the themes of good writing, are not as visible to the naked eye as colors on a marble. The color "red" is relatively easy to see, but the fields in which thematic analysis is often applied do not deal with concepts that can necessarily be seen "objectively." It is up to the researcher to derive themes from the data from an inductive approach. Researchers can also utilize deductive approaches if they want to analyze their data according to themes that have been previously identified in other research.

Think about the picture up above. To the naked eye, these children are holding hands. But themes that can be interpreted from this picture may include "friendship," "happiness," or even "family." The thematic analysis of pictures like this one often depends on a researcher's theoretical commitments, knowledge base, and cultural perspective.

This also means that you are responsible for explaining how you arrived at the themes arising from your data set. While colors are intuitively easy to distinguish, you are often required to explain more subjective codes and themes like "resilience" or "entitlement" so that you and your research audience have a common understanding of your data analysis .

This explanation should account for who you are as a researcher and how you see the data (since, after all, a word like "resilience" can mean different things to different people). A fully reflexive thematic analysis documents and presents where the researcher is relative to their data and to their research audience.

Applications for thematic analysis

Many disciplines within qualitative research employ thematic analysis to make sense of social phenomena. For instances, these fields might be:

• psychotherapy research
• qualitative psychology
• cultural anthropology

In a nutshell, any research discipline that relies on the understanding of social phenomena or insights that may not easily be quantifiable will attract researchers engaged in thematic analysis. Moreover, any exploratory research design lends itself easily to the identification of previously unknown themes that can later be used in a qualitative, quantitative, or confirmatory research project.

Common forms of data collection

Thematic analysis can involve any number of qualitative research methods to collect data, including:

• focus groups
• observations
• literature reviews

Any unstructured data set, particularly any data set that captures social phenomena, can benefit from thematic analysis. The main consideration in ensuring rigor in data collection for thematic analysis is ensuring that your data is representative of the population or phenomenon you are trying to capture.

Virginia Braun and Victoria Clarke are the key researchers involved in making thematic analysis a commonly utilized approach in qualitative research . A quick search for their scholarship will tell you the basic steps involved in thematic analysis:

• Become familiar with the data
• Generate codes from the data
• Generate themes based on the codes
• Review the potential themes
• Define the themes for the final reporting

In a nutshell, thematic analysis requires the researcher to look at their data, summarize their data with codes, and develop those codes to the extent that they can contribute a broader understanding of the context from which the data is collected.

While these are the key points in a robust and rigorous thematic analysis, there are understated parts of the qualitative research process that can often be taken for granted but must never be overlooked to ensure that researchers can analyze their data quickly and with as few challenges as possible.

The process in greater detail

Thematic analysis relies on research questions that are exploratory in nature, thus requiring an inductive approach to examining the data. While you might rely on an existing theoretical framework to decide your research questions and collect all the data for your project, thematic analysis primarily looks at your data inductively for what it says and what it says most often.

After data collection, you need to organize the data in some way to make the data analysis process easier (or, at minimum, possible). A data set in qualitative research is often akin to a crowd of people where individuals move in any direction without any sense of organization. This is a challenge if your research question involves understanding the crowd's age, gender, ethnicity, or style of clothing.

The role of qualitative researchers at this stage is to sort out the crowd. In this example, perhaps this means having the crowd split into different groups according to those demographic identifiers to see which groups are the largest. Reorganizing the crowd from what was previously a group of wandering individuals can offer a better sense of who is in the room.

Qualitative data is often similarly unstructured and in need of reorganization. When dealing with thematic analysis, you need to reorganize the information so that the themes become more apparent to you and your research audience. In most cases, this means reducing the entire data set, as large as it might be, into a more concise form that allows for a more feasible analysis .

Codes and themes are forms of data reduction that address this need. In a thematic analysis involving qualitative data analysis software , researchers code their data by applying short but descriptive phrases to larger data segments to summarize them for later analysis. Later stages of thematic analysis reorganize these codes into larger categories and then themes, where ultimately the themes support contribution to meaningful insights and existing theory.

As you progress in the coding process, you should start to notice that distinct codes may be related to each other. In a sense, codes provide researchers with visual data that they can examine to generate useful themes. ATLAS.ti, for example, lets you examine your codes in the margin to give you a sense of which codes and themes frequently appear in your data. As you code your data, you can apply colors to your codes. This is a flexible method that allows you to create preliminary categories that you can examine visually for their abundance and patterns.

Later on, your codes can be organized into more formal categories or nested in hierarchies to contribute to a more robust thematic analysis.

Especially in qualitative research , discrete analytical approaches overlap with each other, meaning that a sufficiently thorough analysis of your data can eventually yield themes useful to your research. Let's examine a few of the more prominent approaches in qualitative research and their relation to thematic analysis.

Using grounded theory involves developing analysis iteratively through an inductive approach . While there is a great deal of overlap with thematic analysis approaches, grounded theory relies on incorporating more data to support the analysis in previous iterations of the research.

Nonetheless, the analytic process is largely the same for both approaches as they rely on seeking out phenomena that occur in abundance or distinct patterns. As you analyze qualitative data in either orientation, your main consideration is to observe which patterns emerge that can help contribute to a more universal understanding of the population or phenomenon under observation.

Narrative analysis

Understanding narratives is often less about taking large samples of data and more about unpacking the meaning that is produced in the data that is collected. In narrative research analysis , the data set is merely the narrative to be examined for its meaning, intent, and effect on its audience.

Searching for abundant or patterned themes is still a common objective when examining narratives. However, specific questions guide a narrative analysis, such as what the narrator is trying to say, how they say it, and how their audience receives the narrator's message.

Analyzing discourse is similar to analyzing narratives in that there is an examination of the subtext informing the use of words in communication. Research questions under both of these approaches focus specifically on language and communication, while thematic analysis can apply to all forms of data.

The scope of analysis is also different among approaches. Thematic analysis seeks to identify patterns in abundance. In contrast, discourse analysis can look at individual instances in discursive practices to more fully understand why people use language in a particular way.

However, the data resulting from an analysis of discursive practices can also be examined thematically. Discursive patterns within culturally-defined groups and cultural practices can be determined with a thematic analysis when utterances or interactional turns and patterns among them can be identified.

Among all the approaches in this section, content analysis is arguably the most quantitative. Strictly speaking, the words or phrases that appear most often in a body of textual data can tell something useful about the data as a whole. For example, imagine how we feel when a public speaker says "um" or "uh" an excessive number of times compared to another speaker who doesn't use these utterances at all. In another case, what can we say about the confidence of a person who frequently writes, "I don't know, but..."?

Content analysis seeks to determine the frequencies of aspects of language to understand a body of data. Unlike discourse analysis, however, content analysis looks strictly at what is said or written, with analysis primarily stemming from a statistical understanding of the data.

Oftentimes, content analysis is deductive in that it might apply previous theory to new data, unlike thematic analysis, which is primarily inductive in nature. That said, the findings from a content analysis can be used to determine themes, particularly if your research question can be addressed by directly looking at the textual data.

For thematic analysis, software is especially useful for identifying themes within large data sets. After all, thematically analyzing data by hand can be time-consuming, and a researcher might miss nuanced data without software to help them look at all the data thoroughly.

Coding qualitative data

For qualitative researchers, the coding process is one of the key tools for structuring qualitative data to facilitate any data analysis . In ATLAS.ti, data is broken down into quotations or segments of data that can be reduced to a set of codes that can be analyzed later.

The codes and quotations appear in the margin next to a document in ATLAS.ti. This visualization is useful in showing how much of your data is coded and what concise meaning can be inferred from the data. In terms of thematic analysis, however, the codes can be assigned different colors based on what the researcher perceives as categories emerging from their project, as seen in the example above.

As you code the data iteratively, reviewing themes as they emerge, you can organize discrete codes within larger categories. ATLAS.ti provides spaces in your project called code groups and code categories where sets of codes in tandem represent broader, more theoretically developed themes. This approach to data organization , rather than merging codes together as broader units, allows for a more particular analysis of individual codes as your research questions evolve and develop over the course of your project.

ATLAS.ti tools for thematic analysis

As discussed above, analyzing qualitative data for themes can often be a matter of determining which codes and which categories of codes appear across the data and patterns among them. Indeed, any analysis software can assist you with this coding process for thematic analysis. The tools in ATLAS.ti, however, can help to make the process easier and more insightful. Let's look at a few of the many important features that are invaluable to conducting thematic analysis.

Code Manager

The Code Manager is ATLAS.ti's central space where researchers can organize and analyze their codes independent of the raw data . Researchers can perform numerous tasks in the Code Manager depending on their research questions and objectives, including looking just at the data that is associated with a particular code, organizing codes into hierarchies through code categories and nested sub-codes, and determining the frequencies and level of theoretical development for each code.

Co-Occurrence Analysis

Combinations of codes that overlap with each other can also illuminate themes in your data, perhaps more ably than discrete codes. This is different from understanding codes as groups, as an analysis for codes that frequently occur together in the data can give a sense of the relationships between different aspects of a phenomenon.

The Co-Occurrence Analysis tool helps researchers determine co-occurrence between different codes by placing them in a table, a bar chart, a Sankey diagram, or a force-directed graph. These visualizations can illustrate the strength of relationships between codes to you and your research audience. The relationships themselves can also be useful in generating themes useful for your analysis.

Word Frequencies

Qualitative content analysis depends on the frequencies of words, phrases, and other important aspects found in textual data. These frequencies can also help you in generating themes, particularly if your research questions are focused on the textual data itself.

The Word Frequencies tool in ATLAS.ti can facilitate a content analysis leading to a thematic analysis by giving you statistical data about what words appear most often in your project. Suppose these words can contribute to the development of themes. In that case, you can click on these words to find relevant quotations that you can code for thematic analysis. In addition, you can use ATLAS.ti’s Text Search tool to search for data segments that contain your word(s) of interest and automatically code them .

You can also use themes to refine the scope of the Word Frequencies tool. By default, Word Frequencies looks at documents, but the tool also allows researchers to filter the data by selecting the codes relevant to their query. That way, you can look at the most relevant data quotations that match your desired codes for a richer thematic analysis.

Patterns and themes may also emerge from combinations of codes, in which case the Query Tool can help you construct smart codes. Smart codes are more versatile than nested sub-codes or code groups as they allow you to set multiple criteria based on true/false conditions as well as proximity. For example, while a code group simply aggregates distinct codes together to show you quotations with any of the included codes, you can define a set of rules to filter the data and find the most relevant quotations for your thematic analysis.

A systematic and rigorous approach to thematic analysis involves showing your research audience how you arrived at your codes and themes. In qualitative research , visualizations offer clarity about the data in your project, which is a critical skill when explaining the broader meaning derived from otherwise unstructured data .

A TreeMap of codes is a representation of the application of codes relative to each other. In other words, codes that have been applied the most often in your data occupy the largest portions of the TreeMap, while less frequently used codes appear smaller in your visualization. This can give you a sense of the prevalence of certain codes over other codes. Moreover, when you assign colors to codes along the lines of themes and categories, you can quickly get a visual understanding of the themes that appear most often in your project.

As a result, the TreeMap for codes can help provide a visual, thematic map that you can export as an image for use in explaining key themes in your research reports .

In qualitative research , thematic analysis is a useful means for generating a theoretical framework for qualitative concepts and phenomena. As always, though, theoretical development is best supported by thorough research. A theory that emerges from thematic analysis can be affirmed by additional inquiries, whether through a qualitative, quantitative , or mixed methods study .

Further research is always recommended for qualitative research, such as those that employ a thematic analysis, for the very reason that themes in qualitative concepts are socially constructed by the researcher. In turn, future research building on thematic analysis depends on a research design that is transparent and clearly defined so that other researchers can understand how the themes were generated in the first place. This requires a detailed accounting of the data and the analysis through comprehensive detail and visualizations in the final report.

To that end, ATLAS.ti's various tools are specifically designed to allow researchers to share and report their data to their research audiences through data reports and visualizations. Especially where qualitative research and thematic analysis are involved, researchers can benefit from transparently showing their analysis through data excerpts, visualizations , and descriptions of their methodology.

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A Comprehensive Guide to Thematic Analysis in Qualitative Research

What is Qualitative Data?

What do all the methods above have in common? They result in loads of qualitative data. If you're not new here, you've heard us mention qualitative data many times already. Qualitative data is non-numeric data that is collected in the form of words, images, or sound bites. Qual data is often used to understand people's experiences, perspectives, and motivations, and is often collected and sorted by UX Researchers to better understand the company's users. Qualitative data is subjective and often in response to open-ended questions, and is typically analyzed through methods such as thematic analysis, content analysis, and discourse analysis. In this resource we'll be focusing specifically on how to conduct an effective thematic analysis from scratch! Qualitative data is the sister of quantitative data, which is data that is collected in the form of numbers and can be analyzed using statistical methods. Qualitative and quantitative data are often used together in mixed methods research, which combines both types of data to gain a more comprehensive understanding of a research question.

UX Research Methods

There are many different types of UX research methods that can be used to gather insights about user behavior and attitudes. Some common UX research methods include:

• Interviews: One-on-one conversations with users to gather detailed information about their experiences, needs, and preferences.
• Surveys: Online or paper-based questionnaires that can be used to gather large amounts of data from a broad group of users.
• Focus groups: Group discussions with a moderated discussion to explore user attitudes and behaviors.
• User testing: Observing users as they interact with a product or service to identify problems and gather feedback.
• Ethnographic research: Observing and interacting with users in their natural environments to gain a deep understanding of their behaviors and motivations.
• Card sorting: A technique used to understand how users categorize and organize information.
• Tree testing: A method used to evaluate the effectiveness of a website's navigation structure.
• Heuristic evaluation: A method used to identify usability issues by having experts review a product and identify potential problems.
• Expert review: Gathering feedback from industry experts on a product or service to identify potential issues and areas for improvement.

Introduction to Thematic Analysis of Qualitative Data

Thematic analysis is a popular way of analyzing qualitative data, like transcripts or interview responses, by identifying and analyzing recurring themes (hence the name!). This method often follows a six-step process, which includes getting familiar with the data, sorting and coding the data, generating your various themes, reviewing and editing these themes, defining and naming the themes, and writing up the results to present. This process can help researchers avoid confirmation bias in their analysis. Thematic analysis was developed for psychology research, but it can be used in many different types of research and is especially prevalent in the UX research profession.

When to Use Thematic Analysis

Thematic analysis is a useful method for analyzing qualitative data when you are interested in understanding the underlying themes and patterns in the data. Some situations in which thematic analysis might be appropriate include:

• When you have a large amount of qualitative data, such as transcripts from interviews or focus groups.
• When you want to understand people's experiences, perspectives, or motivations in depth.
• When you want to identify patterns or themes that emerge from the data.
• When you want to explore complex and open-ended research questions.
• When you are interested in understanding how people make sense of their experiences and the world around them.

Some UX research specific questions that could be a good fit for thematic analysis are:

• How do users think about their experiences with a particular product, service or company?
• What are the common challenges that a user might encounter when using a product or service, and how do they overcome them?
• How do users make sense of the navigation of a website or app?
• What are the key drivers of user satisfaction or dissatisfaction with a product or service?
• How do users' experiences with a product or service compare with their expectations?

It is important to keep in mind that thematic analysis is just one of many methods for analyzing qualitative data, and it may not be the most appropriate method for every research question or situation. A key part of a UX researcher's role is being aware of the most appropriate research method to use based on the problem the company is trying to solve and the constraints of the company's research practice.

Types of Thematic Analysis

There are two primary types of thematic analysis, called inductive and deductive approaches. An inductive approach involves going into the study blind, and allowing the results of the data-capture to guide and shape the analysis and theming. Think of it like induction heating-- the data heats your results! (OK, we get it, that was a bad joke. But you won't forget now!) An example of an inductive approach would be parachuting onto a client without knowing much about their website, and discovering the checkout was difficult to use by the amount of people who brought it up. An easy theme! On the flip-side, a deductive approach involves attacking the data with some preconceived notions you expect to find in the qualitative data, based on a theory. For example, if you think your company's website navigation is hard to use because the text is too small, you may find yourself looking for themes like "small text" or "difficult navigation." We don't have a joke for this one, but we tried. To get even more nitty-gritty, there are two additional types of thematic analysis called semantic and latent thematic analysis. These are more advanced, but we'll throw them here for good measure. Semantic thematic analysis involves identifying themes in the data by analyzing the exact wording of the comments made used by participants. Latent thematic analysis involves identifying themes in the data by analyzing the underlying meanings and actions that were taken, but perhaps not necessarily stated by study participants. Both of these methods can be used in user research, though latent analysis is more popular because users often say different things than what they actually do.

Steps in Conducting a Thematic Analysis

Let's jump in! As mentioned before, there are 6 steps to completing a thematic analysis.

Step One: get familiar with your data!

This might seem obvious, but sometimes it's hard to know when to start. This might take the form of listening to the audio interviews or unmoderated studies, or reading the notes taken during a moderated interview. It's important to know the overall ideas of what you're dealing with to effectively theme your study. While you're doing this, pay attention to some big picture themes you can use in step two when you code your data. Break out key ideas from each participant. This might take the form of summarized answers for each question response, or a written review of actions taken for each task given. Just make sure to standardize it across participants.

Step Two: sort & code the data.

Now that you have your standardized notes across your participants, it's time to sort and code the collected qualitative data! Think of the themes from before when you were taking your notes. Think of these codes like metaphorical buckets, and start sorting! Every comment that fits a theme in a box, put it there. Back to our navigation example: some codes could be "small text" or "hard to use." We could put a participant action of "squinting" into the bucket for "small text," or a comment from another mentioning they had trouble finding "tents" in "hard to use."

Step Three: break the codes into themes!

Try to think of each theme as a makeup of three or more codes. For the navigation example, we could put both "small text," and "hard to use" into a theme of "Difficult Navigation."

Step Four: review and name your themes.

Now is the time to clean up the data. Are all your themes relevant to the problem you're trying to solve? Are all the themes coherent and straightforward? Are you comfortable defending your theme choices to teammates? These are all great questions to ask yourself in this stage.

Step Five: Present!!

To have a cohesive presentation of your thematic analysis, you'll need to include an introduction that explains the user problem you were trying to identify and the method you took to study it. Use the terminology from beginning of this resource to identify your research method. Usually for something like this, it will be a user survey or interview. ‍ You also need to include how you analyzed your participant data (inductive, deductive, latent or semantic) to identify your codes and themes. In the meaty section of your presentation, describe each theme and give quotations and user actions from the data to support your points.

Step Six: Insights and Recommendations

Your conclusion should not stop at your presentation of your findings. The best user researchers are valuable for both their insights and recommendations. Since UX researchers spend so much time with participants, they have indispensable knowledge about the best way to do things that make life easy for the company's users. Don't keep this information to yourself! On the final 1-3 slides of your presentation, state the "Next Steps & Recommendations" that you'd like your team and leadership to follow up on. These recommendations could include things like additional qualitative or quantitative studies, UX changes to make or test, or a copy change to make the experience clearer for readers. Your ultimate job is to create the best user experience, and you made it this far-- you got this!

And there you have it! That's everything you need to complete a thematic analysis of qualitative data to identify potential solutions or key concepts for a particular user problem. But don't stop there! We recommend using these principles in the wild to conduct research of your own. Identify a question or potential problem you'd like to analyze on one of your favorite sites. Use a service like Sprig to come up with non-bias questions to ask friends and family to try and gather your own qualitative data. Next, complete and document yourself completing the 6-step analysis process. What do you discover? Be prepared to share on interviews-- hiring managers love to see initiative! Good luck.

View the UX Research Job Guide Here

Our Sources: 

Caulfield, J. (2022, November 25). How to Do Thematic Analysis | Step-by-Step Guide & Examples . Scribbr. https://www.scribbr.com/methodology/thematic-analysis/

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• v.21(12); 2021 Dec

General-purpose thematic analysis: a useful qualitative method for anaesthesia research

1 Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland, Auckland, New Zealand

2 Department of Anaesthesia, Auckland City Hospital, Auckland, New Zealand

Learning objectives

By reading this article, you should be able to:

• • Explain when to use thematic analysis.
• • Describe the steps in thematic analysis of interview data.
• • Critique the quality of a study that uses the method of thematic analysis.
• • Thematic analysis is a popular method for systematically analysing qualitative data, such as interview and focus group transcripts.
• • It is one of a cluster of methods that focus on identifying patterns of meaning, or themes, across a data set.
• • It is relevant to many questions in perioperative medicine and a good starting point for those new to qualitative research.
• • Systematic approaches to thematically analysing data exist, with key components to demonstrate rigour, accountability, confirmability and reliability.
• • In one study, a useful six-step approach to analysing data is offered.

Anaesthesia research commonly uses quantitative methods, such as surveys, RCTs or observational studies. Such methods are often concerned with answering what questions and how many questions. Qualitative research is more concerned with why questions that enable us to understand social complexities. ‘Qualitative studies in the anaesthetic setting’, write Shelton and colleagues, ‘have been used to define excellence in anaesthesia, explore the reasons behind drug errors, investigate the acquisition of expertise and examine incentives for hand hygiene in the operating theatre’. 1

General-purpose thematic analysis (termed thematic analysis hereafter) is a qualitative research method commonly used with interview and focus group data to understand people's experiences, ideas and perceptions about a given topic. Thematic analysis is a good starting point for those new to qualitative research and is relevant to many questions in the perioperative context. It can be used to understand the experiences of healthcare professionals and patients and their families. Box 1 gives examples of questions amenable to thematic analysis in anaesthesia research.

Examples of questions amenable to thematic analysis.

• (i) How do operating theatre staff feel about speaking up with their concerns?
• (ii) What are trainee's conceptions of the balance between service and learning?
• (iii) What are patients' experiences of preoperative neurocognitive screening?

Alt-text: Box 1

Thematic analysis involves a process of assigning data to a number of codes, grouping codes into themes and then identifying patterns and interconnections between these themes. 2 Thematic analysis allows for a nuanced understanding of what people say and do within their particular social contexts. Of note, thematic analysis can be used with interviews and focus groups and other sources of data, such as documents or images.

Thematic analysis is not the same as content analysis. Content analysis involves counting the frequency with which words or phrases appear in data. Content analysis is a method used to code and categorise textual information systematically to determine trends, frequency and patterns of words used. 3 Conversely, thematic analysis focuses on the relative importance of ideas and how ideas connect and govern practices. Thematic analysis does not rely on frequency counts to indicate the importance of coded data. Content analysis can be coupled with thematic analysis, where both themes and frequencies of particular statements or words are reported.

Thematic analysis is a research method, not a methodology. A methodology is a method with a philosophical underpinning. If researchers report only on what they did, this is the method. If, in addition, they report on the philosophy that governed what they did, this is methodology. Common methodologies in qualitative research include phenomenology, grounded theory, hermeneutics, narrative enquiry and ethnography. 4 Each of these methodologies has associated methods for data analysis. Thematic analysis can be combined with many different qualitative methodologies.

There are also different types of thematic analysis, such as inductive (including general purpose), applied, deductive or semantic thematic analysis. Inductive analysis involves approaching the data with an open mind, inductively looking for patterns and themes and interpreting these for meaning. 2 , 4 Of note, researchers can never have a truly open mind on their topic of interest, so the process will be influenced by their particular perspectives, which need to be declared. In applied and deductive thematic analysis, the researcher will have a pre-existing framework (which may be informed by theory or philosophy) against which they will attempt to categorise the data. 4 , 5 , 6 For semantic thematic analysis, the data are coded on explicit content, and tend to be descriptive rather than interpretative. 6

In this review, we outline what thematic analysis entails and when to use it. We also list some markers to look for to appraise the quality of a published study.

Designing the data collection

Before embarking on qualitative research, as with quantitative research, it is important to seek ethical review of the proposed study. Ethical considerations include such issues as consent, data security and confidentiality, permission to use quotes, potential for identifying individuals or institutions, risk of psychological harm to participants with studies on sensitive issues (e.g. suicide or sexual harassment), power relationships between interviewer and interviewee or intrusion on other activities (such as teaching time or work commitments). 7

Qualitative research often involves asking people questions during interviews or focus groups. Merriam and Tisdell stated that, ‘The most common form of interview is the person-to-person encounter in which one person elicits information from the other’. 8 Information is elicited through careful and purposeful questioning and listening. 9 Research interviews in anaesthesia are generally purposeful conversations with a structure that allows the researcher to gather information about a participant's ideas, perceptions and experiences concerning a given topic.

A structured interview is when the researcher has already decided on a set of questions to ask. 9 If the researcher will ask a set of questions, but has flexibility to follow up responses with further questions, this is called a semi-structured interview. Semi-structured interviews are commonly used in research involving thematic analysis. The researcher can also use other forms of questioning, such as single-question interview. Semi-structured interviews are commonly used in anaesthesia, such as the studies from our own research group. 10 , 11 , 12

Interviews are usually recorded in audio form and then transcribed. For each interview or focus group, a single transcript is created. The transcripts become the written form of data and the collection of transcripts from the research participants becomes the data set.

Designing productive interview questions

The design of interview questions significantly shapes a participant's response. Interview questions should be designed using ‘sensitising concepts’ to encourage participants to share information that will increase a researcher's understanding of the participants' experiences, views, beliefs and behaviours. 13 ‘Sensitising concepts’ describe words in questions that bring the participants' attention to a concept of research interest. Examples of sensitising concepts include speaking up, teamwork and theoretical concepts (such as Kolb's experiential learning cycle or Foucauldian power theory in relation to trainee learning and operating theatre culture). 14 , 15 Specifically, the questions should be framed in such a way as to encourage participants to make sense of their own experience and in their own words. The researcher should try to minimise the influences of their own biases when they design questions. Using open-ended questions will increase the richness of data. Box 2 gives examples of question design.

How to design an interview question.

Alt-text: Box 2

Bias, positionality and reflexivity

Bias is an inclination or prejudice for or against someone or something, whereas positionality is a person's position in society or their stance towards someone or something. For example, Tanisha once had an inexperienced anaesthetist accidentally rupture one of her veins whilst they were siting an i.v. cannula in an emergency situation. Now, Tanisha has a bias against inexperienced anaesthetists. Tanisha's positionality —a medical anthropologist with no anaesthesia training, but working with many anaesthesia colleagues, including her director—may also inform that bias or the way that Tanisha interacts with anaesthetists. Reflexivity is a process whereby people/researchers proactively reflect on their biases and positionality. Biases shape positionality (i.e. the stance of the researcher in relation to the social, historical and political contexts of the study). In practical research terms, biases and positionality inform the way researchers design and undertake research, and the way they interpret data. It is important in qualitative research to both identify biases and positionality, and to take steps to minimise the impact of these on the research.

Some ways to minimise the influence of bias and positionality on findings include:

(i) Raise awareness amongst the research team of bias and positionality.

(ii) Design research/interview questions that minimise potential for these to distort which data are collected or how they are collected.

(iii) Researchers ask reflexive questions during data analysis, such as, ‘Is my bias about xxx informing my view of these data?’

(iv) Two or more researchers are involved in the analysis process.

(v) Data analysis member check (e.g. checking back with participants if the interpretation of their data is consistent with their experience and with what they said).

Before embarking on the study, researchers should consider their own experiences, knowledge and views; how this influences their own position in relation to the study question; and how this position could potentially introduce bias in how they collect and analyse the data. Taking time to reflect on the impact of the researchers' position is an important step towards being reflective and transparent throughout the research process. When writing up the study, researchers should include statements on bias and positionality. In quantitative research, we aim to eliminate bias. In qualitative research, we acknowledge that bias is inevitable (and sometimes even unconscious), and we take steps to make it explicit and to minimise its effect on study design and data interpretation.

Sampling and saturation

Qualitative research typically uses systematic, non-probability sampling. Unlike quantitative research, the goal of sampling is not to randomly select a representative sample from a population. Instead, researchers identify and select individuals or groups relevant to the research question. Commonly used sampling techniques in anaesthesia qualitative research are homogeneous (group) sampling and maximum variation sampling. In the former, researchers may be concerned with the experiences of participants from a distinct group or who share a certain characteristic (e.g. female anaesthesia trainees), so they recruit selectively from within the group with this shared characteristic to gain a rich, in-depth understanding of their experiences. Conversely, the aim with maximum variation sampling is to recruit participants with diverse characteristics to obtain a broad understanding of the question being studied (e.g. members of different professional groups within operating theatre teams, who have diverse ages, gender and ethnicities).

As with quantitative research, the purpose of sampling is to recruit sufficient numbers of participants to enable identification of patterns or richness in what they say or do to understand or explain the phenomenon of interest, and where collecting more data is unlikely to change this understanding.

In qualitative research, data collection and analysis often occur concurrently. This is because data collection is an iterative process both in recruitment and in questioning. The researchers may identify that more data are needed from a particular demographic group or on a particular theme to reach data saturation, so the next participants may be selected from a particular demographic, or be asked slightly different questions or probes to draw out that theme. Sample size is considered adequate when little or no new information emerges from interviews or focus groups; this is generally termed ‘data saturation’, although some qualitative researchers use the term ‘data sufficiency’. This could also be explained in terms of data reliability (i.e. the researcher is satisfied that collecting more data will not substantially change the results). Data saturation typically occurs with between 12 and 17 participants in a relatively homogeneous sampling, but larger numbers may be required, where the interviewees are from distinct groups or cultures. 16 , 17

Data management

For data sets that involve 10 or more transcripts or lengthy interviews (e.g. 90 min or more), researchers often use software to help them collate and manage the data. The most commonly used qualitative software packages are QSR NVivo, Atlas and Dedoose. 18 , 19 , 20 Many researchers use Microsoft Excel instead, or for small data sets the analysis can be done by hand, with pen, paper and scissors (i.e. researchers cut up printed transcripts and reorder the information according to code and theme). 21 NVivo and Atlas are simply repositories, in which you can input the transcripts and, using your coding scheme, sort the text into codes. They facilitate the task of analysis, rather than doing the analysis for you. Some advantages over coding by hand are that text can be allocated to more than one code, and you can easily identify the source of the segment of text you have coded.

Data analysis

Qualitative data analysis is ‘the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it’. 22

Several social scientists have described this analytical process in depth. 2 , 6 , 22 , 23 , 24 , 25 For inductive studies, we recommend researchers follow Braun and Clarke's practical six-phase approach to thematic analysis. 26 The phases are (i) familiarising the researcher with the data, (ii) generating initial codes, (iii) searching for themes, (iv) reviewing themes, (v) defining and naming themes and (vi) producing the report. These six phases are described next.

Phase 1: familiarising the researcher with the data

In this step, the researchers read the transcripts to become familiar with them and take notes on potential recurring ideas or potential themes. They share and discuss their ideas and, in conjunction with any sensitising concepts, they start thinking about possible codes or themes.

Phase 2: generating initial codes

The first step in Phase 2 is ‘assigning some sort of short-hand designation to various aspects of your data so that you can easily retrieve specific pieces of the data’. 2 The designation might be a word or a short phrase that summarises or captures the essence of a particular piece of text. Coding makes it easier to summarise and compare, which is important because qualitative research is primarily about synthesis and comparison of data. 2 , 25 As the researcher reads through the data, they assign codes. If they are coding a transcript, they might highlight some words, for example, and attach to them a single word that summarises their meaning.

Researchers undertaking thematic analysis should iteratively develop a ‘coding scheme’, which is essentially a list of the codes they create as they read the data, and definitions for each code. 25 , 26 Code definitions are important, as they help the researcher make decisions on whether to assign this code or another one to a segment of data. In Table 1 , we have provided an example of text data in Column 1. TJ analysed these data. To do so, she asked, ‘What are these data about? How does it answer the research question? What is the essence of this statement?’ She underlined keywords and created codes and definitions (Columns 2 and 3). Then, TJ searched the remaining data to see if any more data met each code definition, and if so, coded that (see Table 1 ). As demonstrated in Table 1 , data can be coded to multiple codes.

Table 1

How to code qualitative data: an example

In thematic analysis of interview data, we recommend that code definitions begin with something objective, such as ‘participant describes’. This keeps the researcher's focus on what participants said rather than what the researcher thought or said.

There is no set rule for how many codes to create. 25 However, in our experience, effective manageable coding schemes tend to have between 15 and 50 codes. The coding scheme is iterative. This means that the coding scheme is developed over time, with new codes being created as more data are coded. For example, after a close reading of the first transcript, the researcher might create, say, 10 codes that convey the key points. Then, the researcher reads and codes the next transcript and may, for instance, create additional four codes. As additional transcripts are read and coded, more codes may be created. Not all codes are relevant to all transcripts. The researcher will notice patterns as they code more transcripts. Some codes may be too broad and will need to be refined into two or three smaller codes (and vice versa ). Once the coding scheme is deemed complete and all transcripts have been coded, the researcher should go back to the beginning and recode the first few transcripts to ensure coding rigour.

The second step in Phase 2, once the coding is complete, is to collate all the data relevant to each of these codes.

Phase 3: searching for themes

In this phase, the researchers look across the codes to identify connections between them, with the intention of collating the codes into possible themes. Once these possible themes have been identified, all the data relevant to each possible theme are pulled together under that theme.

Phase 4: reviewing the themes

After the initial collation of the data into themes, the researchers undertake a rigorous process of checking the integrity of these themes, through reading and re-reading their data. This process includes checking to see if the themes ‘fit’ in relation to the coded excerpts (i.e. Do all the data collected under that theme fit within that theme?). Next is checking if the themes fit in relation to the whole data set (i.e. Do the themes adequately reflect the data?) This step may result in the search for additional themes. As a final step in this phase, the researchers create a thematic ‘map’ of the analysis.

When viewed together, the themes should answer the research question and should summarise participant experiences, views or behaviours.

Phase 5: naming the themes

Once researchers have checked the themes and included any additional emerging themes they name the final set of themes identified. Each theme and any subthemes should be listed in turn.

Phase 6: producing the report

The report should summarise the themes and illustrate them by choosing vivid or persuasive extracts from the data. For data arising from interviews, extracts will be quotes from participants. In some studies, researchers also report strong associations between themes, or divide a theme into sub-themes.

Tight word limits on many academic journals can make it difficult to include multiple quotes in the text. 27 One way around a word limit is to provide quotes in a table or a supplementary file, although quotes within the text tend to make for more interesting and compelling reading.

Who should analyse the data?

Ideally, each researcher in the team should be involved in the data analysis. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias. Independent analysis is time and resource intensive. In clinical research, close independent analysis by each member of the research team may be impractical, and one or two members may undertake the analysis while the rest of the research team read sections of data (e.g. reading two or three transcripts rather than closely analysing the whole data set), thus contributing to Phase 1 and Phase 2 of Braun and Clarke's method. 2

The research team should regularly meet to discuss the analytical process, as described earlier, to workshop and reach agreement on the coding and emergent themes (Phase 4 and Phase 5). The research team members compare their perspectives on the data, analyse divergences and coincidences and reach agreement on codes and emerging themes. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias.

Judging the quality and rigour of published studies involving thematic analysis

There are a number of indicators of quality when reading and appraising studies. 28 , 29 , 30 , 31 In essence, the authors should clearly state their method of analysis (e.g. thematic analysis) and should reference the literature relevant to their qualitative method, for example Braun and Clarke. 2 This is to indicate that they are following established steps in thematic analysis. The authors should include in the methods a description of the research team, their biases and experience and the efforts made to ensure analytical rigour. Verbatim quotes should be included in the findings to provide evidence to support the themes.

A number of guides have been published to assist readers, researchers and reviewers to evaluate the quality of a qualitative study. 30 , 31 The Joanna Briggs Institute guide to critical appraisal of qualitative studies is a good start. 30 This guide includes a set of 10 criteria, which can be used to rate the study. The criteria are summarised in Box 3 . Within these criteria lie rigorous methodological approaches to how data are collected, analysed and interpreted.

Ten quality appraisal criteria for qualitative literature.31

• (i) Alignment between the stated philosophical perspective and the research methodology
• (ii) Alignment between the research methodology and the research question or objectives
• (iii) Alignment between the research methodology and the methods used to collect data
• (iv) Alignment between the research methodology and the representation and analysis of data
• (v) Alignment between the research methodology and the interpretation of results
• (vi) A statement locating the researcher culturally or theoretically (positionality and bias)
• (vii) The influence of the researcher on the research, and vice versa
• (viii) Adequate representation of participants and their voices
• (ix) Ethical research conduct and evidence of ethical approval by an appropriate body
• (x) Conclusions flow from the analysis, or interpretation, of the data

Alt-text: Box 3

Another approach to quality appraisal comes from Lincoln and Guba, who have published widely on the topic of judging qualitative quality. 28 They look for quality in terms of credibility, transferability, dependability, confirmability and authenticity. There are many qualitative checklists readily accessible online, such as the Standards for Reporting Qualitative Research checklist or the Consolidated Criteria for Reporting Qualitative Research checklist, which researchers can include in their work to demonstrate quality in these areas.

Conclusions

As with quantitative research, qualitative research has requirements for rigour and trustworthiness. Thematic analysis is an accessible qualitative method that can offer researchers insight into the shared experiences, views and behaviours of research participants.

Declaration of interests

The authors declare that they have no conflicts of interest.

The associated MCQs (to support CME/CPD activity) will be accessible at www.bjaed.org/cme/home by subscribers to BJA Education .

Biographies

Tanisha Jowsey PhD BA (Hons) MA PhD is a senior lecturer in the Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland. She has a background in medical anthropology and has expertise as a qualitative researcher.

Carolyn Deng MPH FANZCA is a specialist anaesthetist at Auckland City Hospital. She has a Master of Public Health degree. She is embarking on qualitative research in perioperative medicine and hopes to use it as a tool to complement quantitative research findings in the future.

Jennifer Weller MD MClinEd FANZCA FRCA is head of the Centre for Medical and Health Sciences Education at the University of Auckland. Professor Weller is a specialist anaesthetist at Auckland City Hospital and often uses qualitative methods in her research in clinical education, teamwork and patients' safety.

Matrix codes: 1A01, 2A01, 3A01

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• How to Do Thematic Analysis | Guide & Examples

How to Do Thematic Analysis | Guide & Examples

Published on 5 May 2022 by Jack Caulfield .

Thematic analysis is a method of analysing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes, topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process:

• Familiarisation
• Generating themes
• Reviewing themes
• Defining and naming themes

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarisation, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences, or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

• How do patients perceive doctors in a hospital setting?
• What are young women’s experiences on dating sites?
• What are non-experts’ ideas and opinions about climate change?
• How is gender constructed in secondary school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyse it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large datasets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

• An inductive approach involves allowing the data to determine your themes.
• A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

There’s also the distinction between a semantic and a latent approach:

• A semantic approach involves analysing the explicit content of the data.
• A latent approach involves reading into the subtext and assumptions underlying the data.

After you’ve decided thematic analysis is the right method for analysing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analysing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or ‘codes’ to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colours corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a condensed overview of the main points and common meanings that recur throughout the data.

Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code ‘uncertainty’ made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the dataset and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them, or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that ‘changing terminology’ fits better under the ‘uncertainty’ theme than under ‘distrust of experts’, since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at ‘distrust of experts’ and determine exactly who we mean by ‘experts’ in this theme. We might decide that a better name for the theme is ‘distrust of authority’ or ‘conspiracy thinking’.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims, and approach.

We should also include a methodology section, describing how we collected the data (e.g., through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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What (Exactly) Is Thematic Analysis?

Plain-Language Explanation & Definition (With Examples)

By: Jenna Crosley (PhD). Expert Reviewed By: Dr Eunice Rautenbach | April 2021

Thematic analysis is one of the most popular qualitative analysis techniques we see students opting for at Grad Coach – and for good reason. Despite its relative simplicity, thematic analysis can be a very powerful analysis technique when used correctly. In this post, we’ll unpack thematic analysis using plain language (and loads of examples) so that you can conquer your analysis with confidence.

Thematic Analysis 101

• Basic terminology relating to thematic analysis
• What is thematic analysis
• When to use thematic analysis
• The main approaches to thematic analysis
• The three types of thematic analysis
• How to “do” thematic analysis (the process)
• Tips and suggestions

First, the lingo…

Before we begin, let’s first lay down some terminology. When undertaking thematic analysis, you’ll make use of codes . A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript.

For example, if you had the sentence, “My rabbit ate my shoes”, you could use the codes “rabbit” or “shoes” to highlight these two concepts. The process of assigning codes is called qualitative coding . If this is a new concept to you, be sure to check out our detailed post about qualitative coding .

Codes are vital as they lay a foundation for themes . But what exactly is a theme? Simply put, a theme is a pattern that can be identified within a data set. In other words, it’s a topic or concept that pops up repeatedly throughout your data. Grouping your codes into themes serves as a way of summarising sections of your data in a useful way that helps you answer your research question(s) and achieve your research aim(s).

Alright – with that out of the way, let’s jump into the wonderful world of thematic analysis…

What is thematic analysis?

Thematic analysis is the study of patterns to uncover meaning . In other words, it’s about analysing the patterns and themes within your data set to identify the underlying meaning. Importantly, this process is driven by your research aims and questions , so it’s not necessary to identify every possible theme in the data, but rather to focus on the key aspects that relate to your research questions .

Although the research questions are a driving force in thematic analysis (and pretty much all analysis methods), it’s important to remember that these questions are not necessarily fixed . As thematic analysis tends to be a bit of an exploratory process, research questions can evolve as you progress with your coding and theme identification.

When should you use thematic analysis?

There are many potential qualitative analysis methods that you can use to analyse a dataset. For example, content analysis , discourse analysis , and narrative analysis are popular choices. So why use thematic analysis?

Thematic analysis is highly beneficial when working with large bodies of data ,  as it allows you to divide and categorise large amounts of data in a way that makes it easier to digest. Thematic analysis is particularly useful when looking for subjective information , such as a participant’s experiences, views, and opinions. For this reason, thematic analysis is often conducted on data derived from interviews , conversations, open-ended survey responses , and social media posts.

Your research questions can also give you an idea of whether you should use thematic analysis or not. For example, if your research questions were to be along the lines of:

• How do dog walkers perceive rules and regulations on dog-friendly beaches?
• What are students’ experiences with the shift to online learning?
• What opinions do health professionals hold about the Hippocratic code?
• How is gender constructed in a high school classroom setting?

These examples are all research questions centering on the subjective experiences of participants and aim to assess experiences, views, and opinions. Therefore, thematic analysis presents a possible approach.

In short, thematic analysis is a good choice when you are wanting to categorise large bodies of data (although the data doesn’t necessarily have to be large), particularly when you are interested in subjective experiences .

What are the main approaches?

Broadly speaking, there are two overarching approaches to thematic analysis: inductive and deductive . The approach you take will depend on what is most suitable in light of your research aims and questions. Let’s have a look at the options.

The inductive approach

The inductive approach involves deriving meaning and creating themes from data without any preconceptions . In other words, you’d dive into your analysis without any idea of what codes and themes will emerge, and thus allow these to emerge from the data.

For example, if you’re investigating typical lunchtime conversational topics in a university faculty, you’d enter the research without any preconceived codes, themes or expected outcomes. Of course, you may have thoughts about what might be discussed (e.g., academic matters because it’s an academic setting), but the objective is to not let these preconceptions inform your analysis.

The inductive approach is best suited to research aims and questions that are exploratory in nature , and cases where there is little existing research on the topic of interest.

The deductive approach

In contrast to the inductive approach, a deductive approach involves jumping into your analysis with a pre-determined set of codes . Usually, this approach is informed by prior knowledge and/or existing theory or empirical research (which you’d cover in your literature review ).

For example, a researcher examining the impact of a specific psychological intervention on mental health outcomes may draw on an existing theoretical framework that includes concepts such as coping strategies, social support, and self-efficacy, using these as a basis for a set of pre-determined codes.

The deductive approach is best suited to research aims and questions that are confirmatory in nature , and cases where there is a lot of existing research on the topic of interest.

Regardless of whether you take the inductive or deductive approach, you’ll also need to decide what level of content your analysis will focus on – specifically, the semantic level or the latent level.

A semantic-level focus ignores the underlying meaning of data , and identifies themes based only on what is explicitly or overtly stated or written – in other words, things are taken at face value.

In contrast, a latent-level focus concentrates on the underlying meanings and looks at the reasons for semantic content. Furthermore, in contrast to the semantic approach, a latent approach involves an element of interpretation , where data is not just taken at face value, but meanings are also theorised.

“But how do I know when to use what approach?”, I hear you ask.

Well, this all depends on the type of data you’re analysing and what you’re trying to achieve with your analysis. For example, if you’re aiming to analyse explicit opinions expressed in interviews and you know what you’re looking for ahead of time (based on a collection of prior studies), you may choose to take a deductive approach with a semantic-level focus.

On the other hand, if you’re looking to explore the underlying meaning expressed by participants in a focus group, and you don’t have any preconceptions about what to expect, you’ll likely opt for an inductive approach with a latent-level focus.

Simply put, the nature and focus of your research, especially your research aims , objectives and questions will  inform the approach you take to thematic analysis.

What are the types of thematic analysis?

Now that you’ve got an understanding of the overarching approaches to thematic analysis, it’s time to have a look at the different types of thematic analysis you can conduct. Broadly speaking, there are three “types” of thematic analysis:

• Reflexive thematic analysis
• Codebook thematic analysis
• Coding reliability thematic analysis

Let’s have a look at each of these:

Reflexive thematic analysis takes an inductive approach, letting the codes and themes emerge from that data. This type of thematic analysis is very flexible, as it allows researchers to change, remove, and add codes as they work through the data. As the name suggests, reflexive thematic analysis emphasizes the active engagement of the researcher in critically reflecting on their assumptions, biases, and interpretations, and how these may shape the analysis.

Reflexive thematic analysis typically involves iterative and reflexive cycles of coding, interpreting, and reflecting on data, with the aim of producing nuanced and contextually sensitive insights into the research topic, while at the same time recognising and addressing the subjective nature of the research process.

Codebook thematic analysis , on the other hand, lays on the opposite end of the spectrum. Taking a deductive approach, this type of thematic analysis makes use of structured codebooks containing clearly defined, predetermined codes. These codes are typically drawn from a combination of existing theoretical theories, empirical studies and prior knowledge of the situation.

Codebook thematic analysis aims to produce reliable and consistent findings. Therefore, it’s often used in studies where a clear and predefined coding framework is desired to ensure rigour and consistency in data analysis.

Coding reliability thematic analysis necessitates the work of multiple coders, and the design is specifically intended for research teams. With this type of analysis, codebooks are typically fixed and are rarely altered.

The benefit of this form of analysis is that it brings an element of intercoder reliability where coders need to agree upon the codes used, which means that the outcome is more rigorous as the element of subjectivity is reduced. In other words, multiple coders discuss which codes should be used and which shouldn’t, and this consensus reduces the bias of having one individual coder decide upon themes.

Quick Recap: Thematic analysis approaches and types

To recap, the two main approaches to thematic analysis are inductive , and deductive . Then we have the three types of thematic analysis: reflexive, codebook and coding reliability . Which type of thematic analysis you opt for will need to be informed by factors such as:

• The approach you are taking. For example, if you opt for an inductive approach, you’ll likely utilise reflexive thematic analysis.
• Whether you’re working alone or in a group . It’s likely that, if you’re doing research as part of your postgraduate studies, you’ll be working alone. This means that you’ll need to choose between reflexive and codebook thematic analysis.

Now that we’ve covered the “what” in terms of thematic analysis approaches and types, it’s time to look at the “how” of thematic analysis.

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How to “do” thematic analysis

At this point, you’re ready to get going with your analysis, so let’s dive right into the thematic analysis process. Keep in mind that what we’ll cover here is a generic process, and the relevant steps will vary depending on the approach and type of thematic analysis you opt for.

Step 1: Get familiar with the data

The first step in your thematic analysis involves getting a feel for your data and seeing what general themes pop up. If you’re working with audio data, this is where you’ll do the transcription , converting audio to text.

At this stage, you’ll want to come up with preliminary thoughts about what you’ll code , what codes you’ll use for them, and what codes will accurately describe your content. It’s a good idea to revisit your research topic , and your aims and objectives at this stage. For example, if you’re looking at what people feel about different types of dogs, you can code according to when different breeds are mentioned (e.g., border collie, Labrador, corgi) and when certain feelings/emotions are brought up.

As a general tip, it’s a good idea to keep a reflexivity journal . This is where you’ll write down how you coded your data, why you coded your data in that particular way, and what the outcomes of this data coding are. Using a reflexive journal from the start will benefit you greatly in the final stages of your analysis because you can reflect on the coding process and assess whether you have coded in a manner that is reliable and whether your codes and themes support your findings.

As you can imagine, a reflexivity journal helps to increase reliability as it allows you to analyse your data systematically and consistently. If you choose to make use of a reflexivity journal, this is the stage where you’ll want to take notes about your initial codes and list them in your journal so that you’ll have an idea of what exactly is being reflected in your data. At a later stage in the analysis, this data can be more thoroughly coded, or the identified codes can be divided into more specific ones.

Step 2: Search for patterns or themes in the codes

Step 2! You’re going strong. In this step, you’ll want to look out for patterns or themes in your codes. Moving from codes to themes is not necessarily a smooth or linear process. As you become more and more familiar with the data, you may find that you need to assign different codes or themes according to new elements you find. For example, if you were analysing a text talking about wildlife, you may come across the codes, “pigeon”, “canary” and “budgerigar” which can fall under the theme of birds.

As you work through the data, you may start to identify subthemes , which are subdivisions of themes that focus specifically on an aspect within the theme that is significant or relevant to your research question. For example, if your theme is a university, your subthemes could be faculties or departments at that university.

In this stage of the analysis, your reflexivity journal entries need to reflect how codes were interpreted and combined to form themes.

Step 3: Review themes

By now you’ll have a good idea of your codes, themes, and potentially subthemes. Now it’s time to review all the themes you’ve identified . In this step, you’ll want to check that everything you’ve categorised as a theme actually fits the data, whether the themes do indeed exist in the data, whether there are any themes missing , and whether you can move on to the next step knowing that you’ve coded all your themes accurately and comprehensively . If you find that your themes have become too broad and there is far too much information under one theme, it may be useful to split this into more themes so that you’re able to be more specific with your analysis.

In your reflexivity journal, you’ll want to write about how you understood the themes and how they are supported by evidence, as well as how the themes fit in with your codes. At this point, you’ll also want to revisit your research questions and make sure that the data and themes you’ve identified are directly relevant to these questions .

Step 4: Finalise Themes

By this point, your analysis will really start to take shape. In the previous step, you reviewed and refined your themes, and now it’s time to label and finalise them . It’s important to note here that, just because you’ve moved onto the next step, it doesn’t mean that you can’t go back and revise or rework your themes. In contrast to the previous step, finalising your themes means spelling out what exactly the themes consist of, and describe them in detail . If you struggle with this, you may want to return to your data to make sure that your data and coding do represent the themes, and if you need to divide your themes into more themes (i.e., return to step 3).

When you name your themes, make sure that you select labels that accurately encapsulate the properties of the theme . For example, a theme name such as “enthusiasm in professionals” leaves the question of “who are the professionals?”, so you’d want to be more specific and label the theme as something along the lines of “enthusiasm in healthcare professionals”.

It is very important at this stage that you make sure that your themes align with your research aims and questions . When you’re finalising your themes, you’re also nearing the end of your analysis and need to keep in mind that your final report (discussed in the next step) will need to fit in with the aims and objectives of your research.

In your reflexivity journal, you’ll want to write down a few sentences describing your themes and how you decided on these. Here, you’ll also want to mention how the theme will contribute to the outcomes of your research, and also what it means in relation to your research questions and focus of your research.

By the end of this stage, you’ll be done with your themes – meaning it’s time to write up your findings and produce a report.

Step 5: Produce your report

You’re nearly done! Now that you’ve analysed your data, it’s time to report on your findings. A typical thematic analysis report consists of:

• An introduction
• A methodology section
• Your results and findings
• A conclusion

When writing your report, make sure that you provide enough information for a reader to be able to evaluate the rigour of your analysis. In other words, the reader needs to know the exact process you followed when analysing your data and why. The questions of “what”, “how”, “why”, “who”, and “when” may be useful in this section.

So, what did you investigate? How did you investigate it? Why did you choose this particular method? Who does your research focus on, and who are your participants? When did you conduct your research, when did you collect your data, and when was the data produced? Your reflexivity journal will come in handy here as within it you’ve already labelled, described, and supported your themes.

If you’re undertaking a thematic analysis as part of a dissertation or thesis, this discussion will be split across your methodology, results and discussion chapters . For more information about those chapters, check out our detailed post about dissertation structure .

It’s absolutely vital that, when writing up your results, you back up every single one of your findings with quotations . The reader needs to be able to see that what you’re reporting actually exists within the results. Also make sure that, when reporting your findings, you tie them back to your research questions . You don’t want your reader to be looking through your findings and asking, “So what?”, so make sure that every finding you represent is relevant to your research topic and questions.

Quick Recap: How to “do” thematic analysis

Getting familiar with your data: Here you’ll read through your data and get a general overview of what you’re working with. At this stage, you may identify a few general codes and themes that you’ll make use of in the next step.

Search for patterns or themes in your codes : Here you’ll dive into your data and pick out the themes and codes relevant to your research question(s).

Review themes : In this step, you’ll revisit your codes and themes to make sure that they are all truly representative of the data, and that you can use them in your final report.

Finalise themes : Here’s where you “solidify” your analysis and make it report-ready by describing and defining your themes.

Produce your report : This is the final step of your thematic analysis process, where you put everything you’ve found together and report on your findings.

Tips & Suggestions

In the video below, we share 6 time-saving tips and tricks to help you approach your thematic analysis as effectively and efficiently as possible.

Wrapping Up

In this article, we’ve covered the basics of thematic analysis – what it is, when to use it, the different approaches and types of thematic analysis, and how to perform a thematic analysis.

If you have any questions about thematic analysis, drop a comment below and we’ll do our best to assist. If you’d like 1-on-1 support with your thematic analysis, be sure to check out our research coaching services here .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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21 Comments

I really appreciate the help

Hello Sir, how many levels of coding can be done in thematic analysis? We generate codes from the transcripts, then subthemes from the codes and themes from subthemes, isn’t it? Should these themes be again grouped together? how many themes can be derived?can you please share an example of coding through thematic analysis in a tabular format?

I’ve found the article very educative and useful

Excellent. Very helpful and easy to understand.

This article so far has been most helpful in understanding how to write an analysis chapter. Thank you.

My research topic is the challenges face by the school principal on the process of procurement . Thematic analysis is it sutable fir data analysis ?

It is a great help. Thanks.

Best advice. Worth reading. Thank you.

Where can I find an example of a template analysis table ?

Finally I got the best article . I wish they also have every psychology topics.

Hello, Sir/Maam

I am actually finding difficulty in doing qualitative analysis of my data and how to triangulate this with quantitative data. I encountered your web by accident in the process of searching for a much simplified way of explaining about thematic analysis such as coding, thematic analysis, write up. When your query if I need help popped up, I was hesitant to answer. Because I think this is for fee and I cannot afford. So May I just ask permission to copy for me to read and guide me to study so I can apply it myself for my gathered qualitative data for my graduate study.

Thank you very much! this is very helpful to me in my Graduate research qualitative data analysis.

Thank you very much. I find your guidance here helpful. Kindly let help me understand how to write findings and discussions.

i am having troubles with the concept of framework analysis which i did not find here and i have been an assignment on framework analysis

I was discouraged and felt insecure because after more than a year of writing my thesis, my work seemed lost its direction after being checked. But, I am truly grateful because through the comments, corrections, and guidance of the wisdom of my director, I can already see the bright light because of thematic analysis. I am working with Biblical Texts. And thematic analysis will be my method. Thank you.

lovely and helpful. thanks

very informative information.

thank you very much!, this is very helpful in my report, God bless……..

Thank you for the insight. I am really relieved as you have provided a super guide for my thesis.

Thanks a lot, really enlightening

excellent! very helpful thank a lot for your great efforts

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Thematic Analysis: What it is and How to Do It

Qualitative analysis may be a highly effective analytical approach when done correctly. Thematic analysis is one of the most frequently used qualitative analysis approaches.

One advantage of this analysis is that it is a versatile technique that can be utilized for both exploratory research (where you don’t know what patterns to look for) and more deductive studies (where you see what you’re searching for).

LEARN ABOUT:  Research Process Steps

This article will break it down and show you how to do the thematic analysis correctly.

What is thematic analysis?

Thematic analysis is a method for analyzing qualitative data that involves reading through a set of data and looking for patterns in the meaning of the data to find themes. It is an active process of reflexivity in which the researcher’s subjective experience is at the center of making sense of the data.

LEARN ABOUT: Qualitative Interview

Thematic analysis is typical in qualitative research. It emphasizes identifying, analyzing, and interpreting qualitative data patterns.

With this analysis, you can look at qualitative data in a certain way. It is usually used to describe a group of texts, like an interview or a set of transcripts. The researcher looks closely at the data to find common themes: repeated ideas, topics, or ways of putting things.

LEARN ABOUT: Data Analytics Projects

Thematic Analysis Advantages and Disadvantages

A technical or pragmatic view of research design focuses on researchers conducting qualitative analyzes using the method most appropriate to the research question. However, there is seldom a single ideal or suitable method, so other criteria are often used to select methods of analysis: the researcher’s theoretical commitments and familiarity with particular techniques.

The thematic analysis provides a flexible method of data analysis and allows researchers with diverse methodological backgrounds to participate in this type of analysis. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.

For positivists, ‘reliability’ is a concern because of the many possible interpretations of the data and the potential for researcher subjectivity to ‘bias’ or distort the analysis. For those committed to the values ​​of steps in qualitative research , researcher subjectivity is seen as a resource (rather than a threat to credibility), so concerns about reliability do not remain.

There is no correct or precise interpretation of the data. The interpretations are inevitably subjective and reflect the position of the researcher. Quality is achieved through a systematic and rigorous approach and the researcher’s continual reflection on how they shape the developing analysis.

Thematic analysis has several advantages and disadvantages. It is up to the researchers to decide if this analysis method is suitable for their research design.

• The flexibility of theoretical and research design allows researchers multiple theories that can be applied to this process in various epistemologies.
• Very suitable for large data sets.
• The coding and codebook reliability approaches are designed for use with research teams.
• Interpretation of themes supported by data.
• Applicable to research questions that go beyond the experience of an individual.
• It allows the inductive development of codes and themes from data.

Disadvantages

• Thematic analysis can miss nuanced data if the researcher is not careful and uses thematic analysis in a theoretical vacuum.
• The flexibility can make it difficult for novice researchers to decide which aspects of the data to focus on.
• Limited interpretive power if the analysis is not based on a theoretical framework.
• It is challenging to maintain a sense of data continuity across individual accounts due to the focus on identifying themes across all data elements.
• Unlike discourse analysis and narrative analysis, it does not allow researchers to make technical claims about language use.

LEARN ABOUT: Level of Analysis

Thematic Analysis Steps

Let’s jump right into the process of thematic analysis. Remember that what we’ll talk about here is a general process, and the steps you need to take will depend on your approach and the research design .

1. Familiarization

The first stage in thematic analysis is examining your data for broad themes. This is where you transcribe audio data to text.

At this stage, you’ll need to decide what to code, what to employ, and which codes best represent your content. Now consider your topic’s emphasis and goals.

Keep a reflexivity diary. You’ll explain how you coded the data, why, and the results here. You may reflect on the coding process and examine if your codes and themes support your results. Using a reflective notebook from the start can help you in the later phases of your analysis.

A reflexivity journal increases dependability by allowing systematic, consistent data analysis . If using a reflexivity journal, specify your starting codes to see what your data reflects. Later on, the coded data may be analyzed more extensively or may find separate codes.

2. Look for themes in the codes.

At this stage, search for coding patterns or themes. From codes to themes is not a smooth or straightforward process. You may need to assign alternative codes or themes to learn more about the data.

As you analyze the data, you may uncover subthemes and subdivisions of themes that concentrate on a significant or relevant component. At this point, your reflexivity diary entries should indicate how codes were understood and integrated to produce themes.

3. Review themes

Now that you know your codes, themes, and subthemes. Evaluate your topics. At this stage, you’ll verify that everything you’ve classified as a theme matches the data and whether it exists in the data. If any themes are missing, you can continue to the next step, knowing you’ve coded all your themes properly and thoroughly.

If your topics are too broad and there’s too much material under each one, you may want to separate them so you can be more particular with your research .

In your reflexivity journal, please explain how you comprehended the themes, how they’re backed by evidence, and how they connect with your codes. You should also evaluate your research questions to ensure the facts and topics you’ve uncovered are relevant.

4. Finalize Themes

Your analysis will take shape now after reviewing and refining your themes, labeling, and finishing them. Just because you’ve moved on doesn’t mean you can’t edit or rethink your topics. Finalizing your themes requires explaining them in-depth, unlike the previous phase. Whether you have trouble, check your data and code to see if they reflect the themes and whenever you need to split them into multiple pieces.

Make sure your theme name appropriately describes its features.

Ensure your themes match your research questions at this point. When refining, you’re reaching the end of your analysis. You must remember that your final report (covered in the following phase) must meet your research’s goals and objectives.

In your reflexivity journal, explain how you choose your topics. Mention how the theme will affect your research results and what it implies for your research questions and emphasis.

By the conclusion of this stage, you’ll have finished your topics and be able to write a report.

5. Report writing

At this stage, you are nearly done! Now that you’ve examined your data write a report. A thematic analysis report includes:

• An approach
• The results

When drafting your report, provide enough details for a client to assess your findings. In other words, the viewer wants to know how you analyzed the data and why. “What”, “how”, “why”, “who”, and “when” are helpful here.

So, what did you find? What did you do? How did you choose this method? Who are your research’s focus and participants? When were your studies, data collection , and data production? Your reflexivity notebook will help you name, explain, and support your topics.

While writing up your results, you must identify every single one. The reader needs to be able to verify your findings. Make sure to relate your results to your research questions when reporting them. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders. You don’t want your client to wonder about your results, so make sure they’re related to your subject and queries.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Because it is easy to apply, thematic analysis suits beginner researchers unfamiliar with more complicated qualitative research . It permits the researcher to choose a theoretical framework with freedom.

The versatility of thematic analysis enables you to describe your data in a rich, intricate, and sophisticated way. This technique may be utilized with whatever theory the researcher chooses, unlike other methods of analysis that are firmly bound to specific approaches. These steps can be followed to master proper thematic analysis for research.

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Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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Policy and Social Care Move Fast: How Rapid Qualitative Methods Can Help Researchers Match Their Pace

Date posted:.

The field of social care integration, which refers to the study and implementation of clinically based programs to address the social needs of patients and families, is advancing at an increasingly rapid pace. This acceleration, driven by heightened need post-pandemic as well as mandates at the state and federal levels for health systems to implement screening and referral programs, has increased the urgency for high-quality evidence to support policy decisions about the delivery of social care—in other words, how health systems identify and address social needs, like access to healthy food and safe housing.  

Qualitative research is particularly useful in guiding social care integration as it can shed light on the patient or caregiver experience of participating in social care interventions, barriers to getting help that should be addressed, and appropriate next steps from the perspective of those directly impacted.

However, traditional qualitative data analysis can be time consuming, and evidence-based solutions for addressing families’ social needs from the clinical setting are needed in the short term. In this post, I’ll share how we adapted and applied rapid qualitative methods to a social care-focused study as an example of how this approach can be used to inform social care integration in real time.

Integrating a Rapid Research Approach

The Socially Equitable Care by Understanding Resource Engagement ( SECURE ) study is a mixed method pragmatic trial aimed at understanding how best to increase family-level engagement with social resources from the pediatric health care setting. Caregivers in the study were randomized to complete one of three different social assessments (surveys asking about their social circumstances and/or desire for social resources) before receiving a resource map on their personal smartphone where, if interested, they could search for community resources in their neighborhood. Caregivers also had the option of talking to our study-specific resource navigator to receive additional support finding resources.

The overall goal of the qualitative component of the study is to capture caregivers’ preferences and experiences receiving social care through SECURE. Our traditional qualitative protocol involved transcribing caregiver interviews verbatim, coding transcripts and conducting thematic analysis. Recognizing the need for implementation-oriented results on a fast timeline, our team explored rapid qualitative methodologies to supplement the traditional approach. The rapid methods we chose were derived from existing literature on rapid qualitative approaches, which were then adapted to suit our study’s protocol and the social care field in general.

In our rapid approach, interviewers took notes using a structured template during or immediately after each caregiver interview. The template was designed to capture the data most salient to social care integration efforts such as caregiver’s likes, dislikes and preferences about receiving social care at their child’s doctor’s office. Then, content from the templates was transposed onto an analytic matrix, where we compared data across participants to identify themes. While we explored the full range of themes that emerged from our caregiver interviews in traditional qualitative analysis, we wanted to be sure that rapid analysis focused on findings that would be most applicable to social care integration efforts so the results could inform social care policy at Children’s Hospital of Philadelphia (CHOP) and elsewhere in real time. For example, what parts of participating in SECURE were helpful for caregivers? Did anything make them uncomfortable?

To ensure that our rapid approach produced results in line with those generated through traditional methods, we analyzed ten of our interviews using both traditional and rapid methods and compared the results. This analysis yielded a 92.8% theme match—meaning the two qualitative methods yielded largely the same themes. This builds upon previous literature, indicating that rapid analysis can be an effective tool in capturing implementation-oriented themes from qualitative data.

How the SECURE Study Can Inform Future Research Efforts

Our rapid qualitative methods allowed us to effectively adapt and respond to the quickly evolving landscape of social care integration, even before we had the full study results. I personally saw this first-hand while working with the SECURE team in 2023 conducting caregiver interviews. For example, we were able to inform hospital efforts in response to a recent insurance requirement of health systems to share caregivers' responses to social screening questions. We successfully gathered patients’ feedback on this new requirement and shared this information and suggestions for what CHOP could do to make caregivers feel more comfortable answering social assessment questions.

While not intended to replace traditional qualitative analysis, being able to produce actionable qualitative findings in a timely manner through rapid methods has allowed SECURE findings to help shape social care interventions at CHOP and beyond in real time.

Our hope is that other researchers in social care who face time pressures may find similar rapid qualitative methods as a useful and effective approach to adapt to the dynamic nature of the field and generate family-centered solutions faster than would otherwise be possible.

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How To Analyse Qualitative Data In Public Consultations: 5 Steps

Citizen engagement

April 24th 2024

By Natasha Martell

Given qualitative analysis’ information-rich text, it can be extremely hard to break it down into its components for analysis.

The lack of structure means governments and public bodies often struggle to collate information from public surveys or consultations effectively and feedback to citizens.

Luckily with the advent of online consultations came the ability to change how we collect and analyse qualitative data, simplifying and organising its output with the help of third-party tools.

Organisations need to know what people are saying, identify common themes and actions, establish public opinion and close the feedback loop by showing their responses have been heard.

Step 1: Preparation

Qualitative analysis in particular asks respondents to take time to write out considered responses. For the most comprehensive results, you want to maximise your qualitative data collection. First, set parameters, conduct market research and establish which research methods will help you achieve this.

Define the goals and objectives you want to achieve from your consultation.

Establish the number of responses the consultation or survey will require from the public for the data collection to provide meaningful insights.

Plan out a mixture of qualitative and quantitative questions that compliment each other and help form the bigger picture.

For example, Picklescott Woodland Creation’s survey asks respondents to rank project objectives in order of importance (quantitative data), and then asked complimentary qualitative questions to provide more in depth information for data analysis.    

Choose a third-party tool that fits requirements. This will help with organising and delineating your data for your qualitative analysis.

Step 2: Gathering Public Feedback for Qualitative Analysis: Best Practices

Feedback is an essential component of qualitative data in government research. During public or statutory consultations , it is essential that governments and public bodies allow citizens to actively participate and provide feedback on suggested policies and changes in their community. With public feedback, government can shape new legislation, policies and agendas in a way that shows they listened meaningfully to public participation.

Good feedback management and collection ensures your qualitative data analysis method is inclusive and insightful, allowing government agencies to make evidence-based decisions that take all viewpoints into account.

Then, in sharing results with the public via We Asked, You Said, We Did, you are closing the feedback loop and showing the consideration you took to qualitative data analysis.

Step 3: Organising & Coding Data for Qualitative Analysis

By using a platform that’s scalable, public officials can collect and organise data from a wide variety of sources and access it in multiple ways.

On Citizen Space, for example, you can receive a PDF summary, docx summary, or detailed analysis that breaks every answer down. If you code responses into types or fields, then you can easily shuffle data into categories for examination.

By including additional functions before sending out reports or surveys for feedback, you can code data into its different factors. Filtering and grouping your qualitative data analysis helps identify key themes or recurring responses that can then be segmented by type for further examination.

With options to filter by participant, their response, your designated codes, keywords etc. data can be qualitatively analysed from multiple angles for a full, complete overview of public response.

Adding codes can make qualitative data quantitative for analysis purposes. Depending on your consultation, you can decide to identify the most important elements of feedback you’re interested in and code them accordingly. From there, Citizen Space will aggregate the applicable answers and they can be downloaded into a spreadsheet or chart/table for further data analysis.

Step 4: Analysing Filtered Qualitative Data

Qualitative data analysis needs to provide actionable insights and establish how organisations can change plans and proposals to better align with public feedback. This holistic approach means public participation is meaningful and consultations provide a clear outcome that considers the needs of the public and stakeholders.

There are five effective qualitative methods you can use to analyse your data:

Content Analysis

A content analysis collates answers that contain instances of the same words, subjects or concepts. This can be achieved by exporting data by certain keywords or codes to identify the frequency of which they are used and what that could mean overall.

Thematic Analysis

Similarly to a content analysis, thematic analysis groups any recurring themes or trends in respondents’ answers. By exporting qualitative responses quantitatively, they can explore the relationship between different aspects of the consultation and how the public perceive they will be impacted.

By identifying these patterns and trends, they can anticipate future responses to public consultations on the topic and predict eventual outcomes.

Narrative Analysis

A narrative analysis process provides a deeper insight into public perception of policies or changes.

This allows for researchers to determine areas of strengths and weaknesses in political planning and create contingencies in future similar cases.

Grounded Theory Analysis

Grounded theory analysis is a qualitative data collection method that begins from scratch. If a consultation is going out with no real knowledge of how people will respond, grounded theory analysis lets you gather and organise information as you go, slowly identifying patterns and trends.

Discourse Analysis

Discourse analysis is a harder to define qualitative research method. Rather than looking at what people are saying it analyses the intent behind it.

This analysis considers cultural and social context and applies it to the data drawn.

Step 5: Reporting back to the public

As we’ve already mentioned, a key step in the ladder of public participation is ensuring respondents feel heard. In order to do that, organisations and public bodies need to close the feedback loop by providing evidence that their opinions were listened to and taken into account.

That’s the exact reason we created Citizen Space’s “We Asked, You Said, We Did” . Using this option, you can share results with the public and keep the entire consultation process transparent and accountable.

When figuring out how to analyse qualitative data in government research, using Delib’s tools and qualitative research methods can ensure your responses are meaningful and provide valuable insight into changes made.

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Meta-thematic synthesis of research on early childhood coding education: A comprehensive review

• Open access
• Published: 24 April 2024

Cite this article

You have full access to this open access article

• Mehmet Başaran   ORCID: orcid.org/0000-0003-1871-520X 1 ,
• Şermin Metin   ORCID: orcid.org/0000-0001-5984-6359 2 &
• Ömer Faruk Vural 3  

The growing significance of coding in 21st-century early childhood education extends beyond technical proficiency, encompassing cognitive development, problem-solving, and creativity. Coding is being integrated globally into educational curricula to prepare students for the digital era. This research examines coding’s potential impact on cognitive and socio-emotional development and emphasizes the need for evidence-based analysis. A meta-thematic analysis synthesizes qualitative data from various studies in a study on coding’s effects on preschool children’s cognitive and socio-emotional development. It focuses on two themes: cognitive contributions and socio-emotional contributions. Thirteen suitable studies were identified from 942 visualized using the PRISMA flow diagram. Coding education enhances cognitive and socio-emotional skills in preschoolers, with implications for curriculum integration. In summary, coding’s holistic benefits in early childhood education are explored, and a meta-thematic analysis investigates its influence on cognitive and socio-emotional domains in preschoolers, emphasizing the need for rigorous evidence-based research.

Avoid common mistakes on your manuscript.

1 Introduction

Technological developments require new generations to acquire specific skills (P21). As technology has become an integral part of our lives, understanding basic computing structures and applications has become essential knowledge required in the 21st century (Czerkawski, 2015 , October). Therefore, it is widely recognized that digital literacy is essential in today’s information society (Barendsen & Stoker, 2013 ). Beyond digital literacy, coding, which refers to using languages that enable computing, is increasingly recognized as a new literacy (Bers, 2020 ; Burke et al., 2016 ; Vee, 2013 ).

When Papert ( 1980 ) developed LOGO, the first programming language to support children’s mathematical skills, he firmly believed that it influenced children’s thinking and led them to think, build, and design in new ways (Papert, 1980 , 2000 , 2005 ). Interest in Papert’s views, which draw attention to the basic concepts of computer science, has increased. This interest has led to the need to enable individuals to take an active and creative role in the use of new cognitive skills and technologies, such as code literacy, and the promotion of programming skills in the early years as essential educational support (Muñoz-Repiso & González, 2019 ). Lin and Weintrop ( 2021 ) stated that computing and the technologies it enables are reshaping the world, and they emphasized that every aspect of our lives is influenced by technology, from how we work and learn to how we play and socialize. Given this increasing presence in our lives, providing opportunities and tools to help people understand how technologies work and train them to control them is becoming an increasing focus of computer education efforts.

Coding is being promoted as a new literacy for all students at all levels of education, including very young children, and is seen as a necessity of the 21st century (Bers, 2019 ; Lye & Koh, 2014 ). For this reason, in recent years, efforts to teach coding and computational thinking, the basic concepts of computer science, in early years and to integrate them into educational processes have increased. These efforts have also accelerated classroom practices and research in this field. However, the studies focus on children’s coding and computational thinking skills (Macrides et al., 2022 ; Papadakis et al., 2016 ; Popat & Starkey, 2019 ). However, Papert ( 1980 ) stated that children’s building using technology and writing code is a new way of thinking for children and that children develop many skills while writing code. For this reason, it is necessary to examine and support the effects of coding on children’s developmental areas in preschool.

Coding is defined as an essential 21st-century skill and literacy that affects all areas of life (Bers et al., 2019 ; McLennan, 2018 ; Monteiro et al., 2021 ; Vee, 2013 ), which is defined as the process of writing the correct syntaxes in a ruleful and sequential manner using command sets and developing applications in order to solve problems, provide human-computer interaction, and enable computers to perform a specific task (Bers et al., 2019 ; Demirer & Sak, 2016 ; Fesakis & Serafeim, 2009 ; Kalelioğlu et al., 2016; Li et al., 2020; McLennan, 2018 ; Vorderman, 2019 ; Wing, 2006 ). Coding is the process of developing systematic ways to solve problems by creating algorithms, which are a set of instructions used to describe each step to perform a specific task or solve a problem (Campell & Walsh, 2017; Ching et al., 2018 ; Lee & Junoh, 2019 ; Lee & Björklund Larsen, 2019 ; McLennan, 2017 ; Vorderman, 2017). The thinking style in coding is seen as the process of numerical thinking, solving problems using algorithms and developing a logical approach, analyzing and organizing data, dividing problems into small and manageable parts, transforming them into specific algorithms, and transforming and organizing them into programming languages (Arabacıoğlu et al., 2007 ; Bers et al., 2019 ; Futschek, 2006 ; Futschek & Moschitz, 2011 ; Gibson, 2012 ; Li et al., 2020; Sullivan et al., 2017 ; Van-Roy & Haridi ( 2004 ).

2.1 Coding in preschool

Coding, a new form of literacy, has become a fundamental tool for reading and interpreting data and communicating with others in a digital society, providing an opportunity to connect children with technology. Thus, coding goes beyond algorithmic thinking and offers children a symbolic language to read and write (Bers, 2018a , 2018b; Mclennan, 2017 ). Despite different conceptual approaches, coding, which is seen not only as a set of technical skills but also as a social and cultural issue involving different fields of knowledge, basically involves thinking like a computer scientist (Grover & Pea, 2018 ), creating and collaborating (Kafai & Burke, 2014 ), and using computing languages, which are especially important for future generations (Monteiro et al., 2021 ). Bers ( 2019 ) argues that, similar to natural languages, children should be introduced to and familiarized with these new artificial languages from an early age. Monteiro et al. ( 2021 ) emphasize that this artificial language should develop children’s perceptual, expressive, and creative skills and lay a strong foundation for developing critical and functional competencies. They also cite understanding “artificial languages” used to create digital structures and transformations as a fundamental skill. In this context, Rushkoff ( 2010 ) states that being able to use the language of computers is emerging as an inevitable skill that allows us to participate fully and effectively in the digital reality that surrounds us. González ( 2015 ) and Bers ( 2019 ) state that individuals will join the new world as code literate when they can read and write in the language of computers and other machines and think numerically.

The literature emphasizes that coding as literacy in preschool education enables the development of personal and social skills that enable children to express, share, and create using computer science languages, ways of thinking, and creativity (Bers, 2020 ; Grover & Pea, 2018 ; Kafai & Burke, 2014 ; Monteiro et al., 2021 ; Resnick & Rusk, 2020 ; Vee, 2013 ). Coding is increasingly recognized as a new literacy that should be encouraged at the right age (Monteiro et al., 2021 ). In recent years, countries and scholars have emphasized the importance and necessity for children to develop the fundamental understandings, skills, and thinking approaches emerging in computer science, such as coding, programming, and computational thinking (García-Valcárcel et al., 2017; Liu et al., 2017 ; Webb et al., 2017 ; Wilson et al., 2010 ). Education stakeholders have begun to emphasize that coding, like mathematics and literacy, is essential for everyone. On January 17, 2018, the European Commission presented a new “Digital Education Action Plan” for Europe to help educational institutions and education systems better adapt individuals to live and work in an era of rapid digital change (Bocconi et al., 2018 ; Webb et al., 2017 ; Wilson et al., 2010 ). The European Commission has also taken an active role in this regard and started to promote coding as today’s literacy (Moreno-León et al., 2015 ).

When the studies on coding skills are examined, it is emphasized that coding provides children with an essential skill necessary for participation in the digital society and contributes to developing all children into computational participants (Kafai & Burke ( 2014 ). In addition, while coding develops children’s critical and creative thinking skills, it also supports their computational competencies (Grover & Pea, 2013 ). The coding process develops problem-solving, reasoning, acquisition of mathematical concepts, meta-cognitive skills (Akyol-Altun, 2018 ; Baytak & Land, 2011 ; Clements & Nastasi, 1999; Çiftçi & Bildiren, 2019; Fessakis et al., 2013 ). (Israel et al., 2015 ; Lai & Yang (2011) Lambert & Guiffre, 2009 ; Sengupta et al., 2013 ); creative thinking skills (Kim, Chunk, & Yu (2013). As Papert ( 1980 ), one of the pioneers of computer science education, emphasized, coding can be generalized for children’s lifelong learning and development, giving them a valuable intellectual structure. In the last decade, numerous research and policy initiatives have focused on the conceptual and technical aspects of introducing coding to young children and the cognitive and social aspects underlying this trend (Monteiro et al.)

Studies on coding in early childhood show that intensive efforts are being made to teach coding skills to children in their early years. It is seen that there have been significant developments in areas such as how to teach coding, instructional approaches, and the assessment of these skills. However, it is necessary to reveal how children and educators conceptualize coding in early childhood and their views on its contribution to development.

When studies on coding skills are examined, coding provides a fundamental skill necessary for participation in the digital society and significantly contributes to children’s developmental areas. According to Papert ( 1980 ), one of the pioneers of computer science education, coding can be generalized for children’s lifelong learning and development. It can equip them with a valuable intellectual structure. In the last decade, numerous research and policy initiatives have focused on the conceptual and technical aspects of introducing coding to young children and the cognitive and social aspects underlying this trend (Monteiro et al.).

2.2 The effect of coding on development

Many countries have incorporated coding education into school curricula (Heintz et al., 2016 ; Hsu, 2019 ). The United States, 16 European countries (Austria, Bulgaria, Czech Republic, Denmark, Estonia, France, Hungary, Ireland, Israel, Lithuania, Malta, Malta, Spain, Poland, Portugal, Slovakia, and the United Kingdom), as well as New Zealand, Australia, Singapore, and Nordic countries have integrated coding into the curriculum at the national, regional, or local level (Bers 2018b; Bocconi et al., 2018 ; Digital News Asia, 2015 ; European Schoolnet, 2015 ). This effort has made coding a new focus of instructional processes starting from early childhood (Bers, 2018a , 2018b; Barron et al., 2011 ; Bers, 2018; CSTA, 2020 ; Grover & Pea, 2013 ; ISTE, 2019 ; NAEYC, 2012 ; US Department of Education, 2010 ; K-12 CSframework, https://k12cs.org/ ).

In recent years, the widespread use of innovative coding platforms, especially screenless programmable robots, has made it possible to integrate coding into early childhood education (Su et al., 2023 ), but classroom applications have not gained momentum. However, Macrides et al. ( 2022 ) and Papadakis et al. ( 2016 ) revealed that these studies were primarily aimed at supporting coding and IS skills. Popat and Starkey ( 2019 ) stated that the revival of coding in the school curriculum promises to prepare students for the future beyond just learning to code. In their review, Popat and Starkey ( 2019 ) found that various other educational outcomes, such as problem-solving, critical thinking, social skills, self-management, and academic skills, can also be learned through teaching coding.

2.3 Effects on cognitive development

There is still a limited understanding of the effects of learning to code on the cognitive development of young children. Although more studies are needed in this area (Relkin et al., 2021 ), studies prove the positive effects of coding on children’s cognitive attitudes, knowledge, and skills (Bers et al., 2014 ; Çiftci & Bildiren, 2020 ; Sullivan & Bers, 2016 ). Coding contributes to developing these skills involving analysis, problem-solving, concept development, transforming problems into specific algorithms and programming languages (García- Peñalvo et al., 2016 ), and spatial reasoning and logic (NAEYC, 2012 ). García- Peñalvo et al. (2016) argued that since children develop their thinking skills through language, learning to use a programming language involving logical sequencing, abstraction, and problem-solving also supports their analytical thinking skills. In a rapidly changing digital society, coding is thought to be useful for children to develop computational thinking skills (Bers et al., 2014 ; Chou, 2020 ), mathematical thinking (Goldenberg & Carter, 2021 ), problem-solving, critical thinking, and higher order thinking (Ackermann, 2001 ; Bers et al., 2002 ; Bers, 2010 ; Bers & Horn, 2010 ; Clements & Gullo, 1984 ; Clements & Meredith, 1993 ; Kazakoff & Bers, 2012 ; Lee et al., 2013 ; Popat & Starkey, 2019 ; Portelance et al., 2016 ; Strawhacker et al., 2015 ).

Coding helps develop cognitive abilities such as systematic thinking, problem-solving, relationships between events, and creative thinking (Fesakis & Serafeim, 2009 ). For this reason, studies are showing that coding practices contribute significantly to children’s cognitive development (Grover & Pea, 2013 ; Kazakoff & Bers, 2012 ; Kazakoff et al., 2013 ; Papadakis et al., 2016 ). Recent studies on this subject have examined cognitive development (Flannery et al., 2013), sequencing skills (Caballero-Gonzalez et al., 2019; Kazakoff et al., 2013 ; Kazakoff & Bers, 2014 ), problem-solving skills (Akyol-Altun, 2018 ; Bers et al., 2014 ; Fessakis et al., 2013 ; Koç, 2019; Saxena et al., 2020 ), executive functions (Di Lieto et al., 2017 ), creativity (Flannery & Bers, 2013; Resnick, 2006 ; Siper-Kabadayı, 2019 ; Sullivan & Bers, 2017, 2019 ; Wang et al., 2011 ), and computational thinking (Batı, 2022; Bers et al., 2014 ; Bers et al., 2019 ; Caballero-Gonzalez et al., 2019; Kalogiannakis & Papadakis, 2017 ; Kazakoff et al., 2013 ; Papadakis et al., 2016 ), and visuospatial skills (Bers et al., 2014 ; Flannery et al., 2013).

2.4 Effect on social-emotional development

Bers ( 2020 ), who sees coding as another language and a new literacy and presents its general framework, refers to coding as “expressive symbolic systems” and “computational thinking tools.” However, she emphasizes that focusing only on information processing ignores the symbolic language aspect of coding, an expressive tool and that a language can be a language when it has a social and a mental side. Moreover, she emphasizes that coding as literacy should include not only thinking like a natural language but also expression and communication or social interaction, which involves doing, creating, and bringing into being. Bers ( 2008 ) states that coding, like writing, is a tool for human expression and emphasizes that in this process, children seek new ways of thinking and expressing new ideas and develop new thinking, feeling, and communication skills through this impressive process.

Coding provides the necessary motivation for children to learn programming in more detail and supports their emotional aspects by enabling them to transform ideas into products (Heikkilä, 2020 ; Toh et al., 2016 ). Machines have become a part of our lives, and we communicate with them just as we do with other individuals. For this reason, García- Peñalvo et al. (2016) stated that coding enables children to collaborate better with machines.

Fox and Farmer ( 2011 ) state that children not only manipulate objects and learn rules while creating concrete products through coding but also write codes, build artifacts in virtual environments, and review, share, and revise them. For this reason, it is emphasized that coding activities allow students to cooperate with their peers and provide highly sustainable participation in problem-solving and reasoning (Fox & Farmer, 2011 ). Studies have found that computers can act as a catalyst for social interaction in early childhood education classrooms (Clements, 1999 ) and that children have twice as much social interaction in front of computers as in other activities (Svensson, 2000 ) and speak twice as many words as in non-technology-related activities (New & Cochran, 2007 ). Coding education, whether provided through block-based applications or robotic tools and activities, can improve children’s peer collaboration, communication, and social relations (Bers et al., 2019 ; Lee et al., 2013 , 2017 ; Sullivan & Bers, 2018 ; Wartella & Jennings, 2000 ), social development and socially oriented development (Bers, 2012 ; Caballero-Gonzalez et al., 2019; Critten et al., 2022 ; Fessakis et al., 2013 ; Flannery et al., 2013; Pugnali et al., 2017 ; Strawhacker & Bers, 2015 ) and self-regulation skills (Kazakoff, 2014 ).

The findings of this study provide evidence that coding contributes to some children’s developmental areas. In addition, the opinions and perceptions of the participants regarding coding are also seen as a factor that will contribute to the field. The views of children who receive coding education and teachers who work with children on the effects of coding on development are considered necessary to guide the studies conducted in this field and the practices and curricula to be developed.

2.5 Review studies on coding

Many systematic analysis studies have been conducted on coding at the K-12 level. Lye and Koh ( 2014 ), who conducted one of these studies, revealed that empirical studies on early childhood are lacking. However, since Lye and Koh ( 2014 ) drew attention to the deficiency in the field of early childhood, it is seen that studies in this field have increased rapidly. With this increase, the studies conducted in this field have started to be analyzed. There are a limited number of review studies conducted for preschool children. Papadakis et al. ( 2016 ) present a literature review including 18 studies on how the ScratchJr application affects children’s CT, coding, and general literacy skills in preschool. The study emphasized that ScratchJr seems to be a helpful application that positively affects children’s IT and coding skills. Popat and Starkey ( 2019 ) included 11 studies in their review study to analyze the educational outcomes of children learning coding at school. Of these studies, only one was on the problem-solving skills of 5-6-year-old children. Other studies are primarily studies for primary school children. Popat and Starkey ( 2019 ) stated that the studies show that students can learn coding and that they can learn several other educational outcomes (such as mathematical problem-solving, critical thinking, social skills, self-management, and academic skills) through coding instruction.

Sulistyaningtyas et al. ( 2021 , September) reviewed 9 studies on coding for early childhood children between 2015 and 2020. This review includes two main objectives: coding practices in early childhood and the impact of coding on early childhood development. In the study, unplugged and plugged activities were used in early childhood, and Children’s planning and inhibition skills in communication, collaboration, and creativity were stated as learning outcomes. Macrides et al. ( 2022 ) analyzed the studies on programming in early childhood education. This review study analyzed 34 studies for children aged 3–8 years. Of these studies, 5 were conducted with children over 6. These findings show that there has been a significant increase in studies on preschool children in recent years. The intervention programs examined in these studies primarily focus on teaching coding (11 studies) and IT skills (11 studies), with limited attention given to supporting children’s overall development. Among the studies targeting developmental areas, the emphasis is mainly on cognitive aspects, particularly problem-solving and creativity. Zurnacı and Turan ( 2022 ) reported that, in Turkey, there were 30 studies on preschool coding, consisting of 11 qualitative, 11 quantitative, and 4 mixed-methods studies. These studies predominantly address coding and IT skills but also address academic, cognitive, language, and social skills.

Su et al. ( 2023 ) reviewed 20 studies on early childhood coding curricula published in 2012–2021. In this study, educational practices for children were examined in depth. In this review, how the curricula in educational practices for children are designed, which coding platforms or applications are used, what pedagogical approaches are used, research methods, and findings obtained from these studies were examined in depth. In recent years, educational approaches to support preschool children’s coding skills have increased, and robotics, Web 2.0 tools, and web-based applications have been developed to support children’s coding skills. These studies have revealed that children can acquire coding skills early on. However, it is essential to examine how coding skills contribute to children’s other developmental areas and to develop research and applications in this field. This review of coding has contributed significantly to the current state of the art in this field, as well as the needs and future research. Resnick and Rusk ( 2020 ) note that over the past decade, they have seen that it is possible to extend coding experiences to millions of children worldwide. At the same time, they emphasize that there are extraordinary challenges, that coding has been introduced in ways that undermine its potential and promise in many places, and that educational strategies and pedagogies to introduce coding must be carefully discussed. For this reason, in addition to the quantitative data on coding, it is thought that knowing how teachers and children interpret coding can shed light on similar future studies. For this reason, this study aims to shed light on future studies by comprehensively examining qualitative studies on preschool children and the effects of coding on children’s developmental areas in these studies.

3 Methodology

3.1 research model.

This research endeavors to ascertain the impact of coding instruction on preschool-aged children’s cognitive and socio-emotional development. The primary objective of this investigation is to undertake a systematic analysis of qualitative primary data, discerning recurring themes and topics elucidating the effects of coding education on children’s development. This analytical process culminates in synthesizing these identified themes and topics, ultimately facilitating the derivation of comprehensive conclusions. In the context of this research, the meta-thematic analysis approach is recurrently utilized to meticulously dissect the primary qualitative data (Thomas & Harden, 2008 ). Specifically, this study adopts a meta-thematic framework to synthesize qualitative studies concerning preschool children and their engagement with coding education. Within the purview of the meta-thematic analysis, three overarching themes are meticulously examined:

Theme 1: “What are the cognitive ramifications of incorporating coding education in preschool settings?”

Theme 2: “What are the socio-emotional implications stemming from integrating coding education in preschool contexts?”

Theme 3: “ What are the comparisons of theses data and research articles data ?”

These themes provide the structural foundation for the comprehensive investigation into the multifaceted impacts of coding education on preschool-aged children’s cognitive and socio-emotional development.

3.2 Studies included in the study

In this study, studies on coding education at the preschool education level were investigated within the scope of meta-thematic analysis. The criteria for the inclusion of the study in the meta-thematic analysis were determined as follows:

Being at the level of preschool education (0–6 years),

Aiming to measure the effects and limitations of coding education on students’ cognitive, emotional, and social context,

Scientifically qualified and sufficient,

Including direct participant views,

Being an experimental study,

Being a thesis or article,

The studies were selected according to these criteria.

In the study, seven databases, including “Science Direct-SD,” “Taylor and Francis-TF,” “Higher Education Council Thesis Center (YokTez-YT),” “Dergipark,” “ProQuest-PQ,” ERIC-E,” and “Web of Science-WOS,” were utilized. The databases were searched with the keywords “preschool coding,” “early childhood coding,” “computer-free coding,” “preschool programming,” and “early childhood programming.”

The articles and theses searched in the database were selected based on the above criteria. At the end of this study, 942 studies had been reached. Based on the criteria at the end of the evaluation, 13 articles were included in the meta-thematic analysis. The number of included and excluded studies in the meta-thematic analysis is presented in Fig.  1 using the PRISMA flow diagram (Moher et al., 2009 ).

Flow diagram of the studies included in the meta-thematic analysis

According to the criteria presented in the PRISMA flow diagram in Fig.  1 and 942 studies examining the research topic were reached. Based on the evaluation according to the research criteria, some studies were eliminated by not being included in the meta-thematic analysis. Two of the studies scanned in the databases were eliminated due to duplication. Another 653 studies were eliminated from the remaining studies due to irrelevant topics. Of the remaining 287 studies, 182 studies were eliminated because they were not suitable for the primary purpose as a result of abstract screening. Of the remaining 105 studies, 88 were eliminated due to qualitative evaluation. Of these studies, 62 were eliminated because there was no qualitative interview data, and 26 were eliminated because there was no experimental study. Among the remaining 17 studies, as a result of the research conducted at the level of the findings, it was determined that the data of four studies needed to be sufficient and appropriate in terms of content and were eliminated. Thus, 13 studies were reached as a result of the screening. This study is limited to 13 studies accessed during the meta-thematic analysis process and included in the analysis. Although this situation is considered a limitation of the study, it follows the nature of meta-thematic studies (Batdı, 2017 , 2019 ).

The reasons for not including the studies that were not included in the meta-thematic analysis are shown in Table  1 . Accordingly, 942 studies were collected from 7 databases, and 929 were eliminated for the reasons shown in Table  1 . 13 studies were included in the meta-thematic analysis.

General information on the articles and the theses used in this study is given in Table  2 below.

The provided sources offer a diverse range of perspectives and insights on the integration of coding into education. Despite this diversity, the common thread across all sources is their emphasis on the importance and benefits of integrating coding into educational settings. They highlight how this integration can address various challenges educators face, such as teaching abstract concepts, fostering creativity, and enhancing problem-solving skills among students. Moreover, the sources underscore the significance of providing resources and support for educators to incorporate coding into their teaching practices effectively. However, differences emerge in the themes explored and the depth of analysis offered. For instance, some sources delve into the practical challenges educators face in implementing coding activities (E1, SD), while others focus on the pedagogical benefits and implications of such integration (WOS, PQ). Overall, while the sources vary in their approach and emphasis, they collectively advocate for integrating coding as a valuable tool for enhancing education and preparing students for the demands of the digital age.

The codes obtained in the meta-thematic analysis related to coding education in preschool were grouped under three themes. In this context, the titles “Contributions of coding education in preschool to the cognitive domain,” “Contributions of coding education in preschool to a social-emotional domain,” and “Comparision of theses data and research articles data” were accepted as themes.

In the current study, the theme created by the researcher related to the research topic and the codes that make up the theme were discussed separately and presented with the findings. At the same time, in interpreting the findings, the sources from which the codes were referenced were directly quoted and supported by the presentation of the themes and codes.

4.1 Contributions of coding education in preschool to the cognitive domain

In the meta-thematic analysis, the sub-problem of the research, “Contributions of coding education in preschool to the cognitive domain,” was taken as a theme. Participant opinions were analyzed in the studies, and codes were created regarding their statements. Codes were created for features such as coding education in preschool, developing students’ intelligence, developing cognitive skills, and reinforcing what is learned.

Contributions of coding education in preschool to the cognitive domain

As a result of the meta-thematic analysis, three sub-categories and ten codes were reached under the theme “Contributions of Coding Education in Preschool to Cognitive Domain.” These codes are shown in Fig.  2 ; Table  3 with the frequency and percentage values. Two experts (academicians) from the field of educational sciences worked on the codes and grouped them into three sub-themes.

The skills development sub-category covers the skills that students are expected to develop, especially those widely referred to as 21st-century skills. During the coding process, it was observed that students especially developed these skills. The codes in the learning enhancement sub-category cover the skills that need to be acquired in daily life and learning towards the permanent learning process. In this case, it is an essential skill that emerges in the final learning process. Interdisciplinary contribution is an important dimension in education that is becoming increasingly important today. In this study, it emerged as a sub-dimension, albeit a very small one.

Table  3 shows that the codes are grouped around three sub-categories. Among these sub-categories, skills development has the highest rate, with 75.3%. Learning enhancement is the sub-category with the second highest rate of 23.6%. Interdisciplinary contribution is the sub-category with the lowest rate of 1.1%. In this context, it can be said that coding education develops skills in preschool children in general.

These codes belong to the skills development sub-category. The contribution of coding education in the cognitive dimension was to develop problem-solving skills with 26.4% and directing (commanding) skills with 24.7%. This skill can also be expressed as a computational thinking skill. This code emerged from the statements about students giving commands to the robot or computer and directing it. In the thesis coded YT3-p.73, the statement “ Then it would be like this. First, I program it to turn silently, then play a birthday song, and then turn it off .” “ It is to teach ways to tell tools such as computers and phones what to do. ” In the article coded E3-p.10, the statement “ I need to stick the arrows in the right direction and take this character to dinner by following the path… ” can be shown as an example.

The code for problem-solving skills was found 47 times in the studies. Some of the statements referenced in this code are “ I believe that it will contribute to the development of children’s abilities in areas such as thinking skills, logic development, problem-solving, etc .” in the article coded E2- p.753. In the thesis coded YT2-p.55, the statement “ It is an approach that provides problem-solving, creativity and analytical thinking skills. ” can be given as examples.

For the code related to the development of creativity: in the thesis coded YT6-p.117, the statement “ They did not have difficulty in applying the new rule as before, they created new rules themselves and turned this situation into a new game ” in the thesis coded YT1-p.68, the statement “ We adjust those things when we press it, it does the coding we want, it does the coding according to our imagination .” and in the article coded PQ-p.304, the statement “ It develops creative thinking and improves cooperative learning. It was collaborative training because we carried out the activities in two groups.”

These codes serve as crucial indicators of the impact of coding education on cognitive dimensions, showcasing its role in enhancing problem-solving skills, directing abilities (such as computational thinking), and fostering creativity among students. They are supported by specific statements and instances extracted from the qualitative research studies, demonstrating real-world applications and observations.

These codes belong to the learning enhancement sub-category. The references related to the code of transferring to daily life: in the thesis coded YT4-s.119, the statement “ There were touches about life-related to the general program. In other words, you always tried to associate it with life rather than sitting down and doing fashion mode robotics training…” and in the thesis coded YT3-p.76, the statement “ They reach places that we cannot reach… For example, lifting large items… ” can be given as examples.

Regarding the effective learning code: In the article coded E1-p.63, the statement “ Taking some concepts through disconnected activities that they already had some experience with and using them to apply them with technology helped them respond quickly and understand better .” can be given as an example.

Codes related to permanent learning: In the thesis coded YT6-p.115, statements “ They did not forget the order of events in the story. Each child made small changes in the story for his/her next friend, and the other child had no difficulty remembering or practicing .” Regarding the code of facilitating learning: In the thesis coded YT4- p.120, the statement “…They had much difficulty in the activities we did about graphics. At the end of the training process, they were able to do such activities much more easily. ” can be given as an example. Regarding the statement in which the code for being comprehensive was revealed: In the article PT4- p.119, the statement “ The activities in the implemented education program were very comprehensive and numerous. Turkish language, art, science, mathematics, drama, play, etc. activities in the preschool program were all included, . .” can be given as an example.

These codes collectively illustrate how coding education transcends theoretical learning, promoting practical application in daily life, improving learning efficacy, supporting long-term knowledge retention, enhancing skill mastery, and contributing to a comprehensive educational experience across different subject areas.

These codes belong to the interdisciplinary contribution sub-category. For the code of contributing to different disciplines: in the article coded PQ-p.311, the statement “ For example, I can use it in animals, colors, shapes, internal organs, and mathematics activities. ” can be given as an example. Regarding the code for the development of intelligence and manual skills: in the article coded E2-p.755, the statement “ I think it was beneficial for the development of intelligence. Being careful helped a lot in the development of manual skills. I also believe using the materials will improve the sensory organs .” can be shown as an example.

These codes emphasize the broad spectrum of benefits associated with coding education. They show how coding contributes to diverse subject areas and is pivotal in enhancing cognitive abilities, fostering manual dexterity, and potentially improving sensory perception through materials and hands-on experiences.

4.2 Contributions of coding education in preschool to the social-emotional domain

In the meta-thematic analysis, the sub-problem of the study, “ Contributions of coding education in preschool to the social-emotional domain ,” was taken as a theme. The participants’ opinions in the articles and theses obtained from the research were examined, and codes were created regarding their statements. Codes such as motivating, fun, and cooperative learning were created for coding education in preschool. As a result of the meta-thematic analysis, eight codes were found under the theme “Contributions of coding education in preschool to a social-emotional domain.” These codes are given in Fig.  3 . In addition, Table  4 below shows the frequency and percentage values of the codes.

Contributions of coding education in preschool to the social-emotional domain”

As a result of the meta-thematic analysis, two sub-categories and eight codes were reached under the theme “Contributions of Coding Education in Preschool to Social-Emotional Domain.” These codes are shown in Fig.  3 ; Table  4 with the frequency and percentage values. Two experts (academicians) from the field of educational sciences worked on the codes and grouped them into two sub-themes.

These sub-categories encompass crucial facets of comprehensive growth. Social and behavioral development entails the acquisition of proficiencies indispensable for efficacious engagement, collaboration, and adjustment in diverse social contexts. Personal development and empowerment concentrate on individual advancement, nurturing resilience, self-assurance, and self-governance to empower individuals to navigate life with certitude. In unison, these categories epitomize manifold dimensions of human maturation and skill enhancement.

Table  4 shows that the codes are grouped around two sub-categories. Social and behavioral development has the highest rate among these sub-categories, with 76.5%. Personal development and empowerment is the sub-category with the second highest rate of 23.5%. In this context, it can be said that coding education develops social-emotional aspects in preschool children in general.

These codes belong to the social and behavioral development sub-category. The code with the highest percentage value was the code of being fun, with 25.9%. Codes related to being fun: In the thesis coded YT6-p.118, the statement “ They had much fun in the game of reaching the nest through obstacles. They put the obstacles in different places and continued to play. ” and in the article coded PQ-p.309, the statement “ It should be included in the school curriculum. It provides cognitive thinking as it both entertains and provides problem-solving skills and even cooperation… ” can be given as an example.

The codes related to supporting cooperative learning and communication can be referenced as follows: “ In the field of social-emotional development, the fact that children look for solutions together, communicate and help each other during programming activities supports the development of collaborative attitude in children .” in the thesis coded YT2- p.64 and “… The fact that group activities were given much space and the groups were mixed strengthened their communication .” the thesis coded YT4- p.120 can be given as examples. Regarding the curiosity code: In the thesis coded YT5- p.78, the statement “ I want to place the cubes immediately for my character to move. ” can be exemplified.

These codes underscore how coding endeavors impart technical proficiencies and yield considerable benefits towards cultivating intangible skills, such as collaboration, proficient communication, and inherent drive and intellectual inquisitiveness, among students.

These codes belong to the personal development and empowerment sub-category. In the present study, 9.9% was found for the code of increasing motivation. The statement “ They were also eager to put the blocks together to create different dances .” In the articles WOS- p.341 and SD- p.142, the statement “… KIBO was an extraordinary source of motivation for our students” can be cited as examples. About the code related to gaining responsibility: In the article SD- p.141, the statement “…Progress was made in supporting values such as respect for a partner and their ideas, the ability to wait, the development of responsibility and autonomy, and the care of materials… ”. Regarding the code for increasing self-confidence: In the article PT2- p.64, the statement “… Learning new things makes children feel good and increases their self-confidence. They express that they are happy after the activity. ” can be given as an example. Referring to the codes related to providing focus: In the article coded E2- p.754, the statement “ The application contributed to the development of children in areas such as cooperation, sharing, focusing and attention… ” can be exemplified.

These codes highlight how coding education transcends technical skills, fostering personal growth by enhancing motivation, instilling a sense of responsibility, boosting self-confidence, and refining essential behavioral attributes like focus and attention.

4.3 Comparision of theses data and research articles data

When the studies are classified as theses and articles and analyzed in terms of similarities and differences, similarities and differences in Target Age Group, Learning Focus, Main Tools, Activities, Benefits, Challenges, Educational Impact, and Teacher Involvement are given in the table in detail (Table 5 ).

The data of research articles delves into the educational application of robotics and coding activities, primarily aimed at young children in preschool and early elementary school. The emphasis is on hands-on learning experiences integrating technology tools such as KIBO and Bee-Bot into the classroom environment. These tools are designed to introduce children to foundational concepts of programming and computational thinking playfully and interactively.

One of the key observations from the research articles’ data is the positive impact of these activities on various aspects of child development. Through engaging with robotics and coding, students demonstrate enhanced teamwork by collaborating with peers to solve problems and complete tasks. The iterative nature of these activities encourages perseverance and determination as students persist in their efforts to achieve success, boosting their confidence along the way.

Teachers and researchers also note the benefits of using structured materials, such as wooden blocks, in conjunction with technology tools. These materials provide tangible, hands-on experiences that help students develop spatial reasoning, problem-solving, and fine motor skills. Moreover, using concrete materials ensures that learning activities are accessible and engaging for all students, regardless of their prior experience or background knowledge.

However, integrating robotics and coding into the curriculum presents its own set of challenges. Educators highlight the importance of starting with unplugged, concrete activities to build foundational understanding before introducing technology-based tools. They also stress the need for adequate teacher training and resources to support effective implementation, particularly in designing developmentally appropriate activities and scaffolding learning experiences to meet the diverse needs of students.

In summary, the data from the research articles underscores the potential of robotics and coding activities to foster critical thinking, collaboration, and creativity among young learners. By providing hands-on experiences with technology tools, educators can help students develop essential skills for success in the digital age while promoting a positive attitude towards learning and exploration. However, achieving these goals requires careful planning, ongoing support, and a commitment to inclusive and equitable education for all students.

Theses data centers around educational activities promoting active participation, problem-solving skills, and curriculum integration. Teachers engage students in diverse activities that target various learning outcomes, including motor skills and cognitive development. These activities are adaptable for different age groups and subjects, allowing for flexibility in implementation.

Teachers reflect on the effectiveness of these activities, considering factors such as student engagement, comprehension, and skill acquisition. While the specific nature of the activities is not detailed, they likely involve hands-on experiences, group collaboration, and exploration of different concepts.

Overall, theses’ data highlight the importance of engaging students in interactive and multidimensional learning experiences that cater to their developmental needs and enhance their understanding of various subjects.

5 Discussion

The fact that computer science is seen as a skill that all individuals should acquire in the early years has increased interest in coding. In addition, innovative coding platforms such as screenless programmable robotics, which have increased in importance in recent years to support 21st-century skills and STEM skills, have increasingly entered children’s early years (Macrides et al., 2022 ). This growing interest in the necessity of coding has increased the efforts of countries to integrate coding into their educational curricula. This increase has also accelerated research in this field. The view that coding is not only about teaching computer science concepts to children but also about skills and literacy has started to gain importance. The view that coding is a skill that provides children with a new perspective, way of thinking, and behavior has been emphasized. However, Popat and Starkey ( 2019 ) and Su et al. ( 2023 ) emphasize that recent studies on coding in early childhood have mainly focused on children’s coding or computational thinking. Su et al. ( 2023 ) pointed out that there are limited studies on the effects of coding on development and that studies should be conducted in this field. Therefore, in this study, qualitative studies on coding were examined to reveal the effects of coding on development. This study has analyzed qualitative studies, considering that they will contribute significantly to this emerging field by examining the work done in this area, what needs to be done in the future, and what kinds of gaps exist.

The meta-thematic analysis aimed to answer the primary research question: “What are the contributions of coding in early childhood education to the cognitive domain?” The findings indicate opinions that coding contributes to directive (command-giving) skills, problem-solving abilities, and fostering creativity. Cognitive-weighted learning outcomes such as transferring knowledge to daily life, effective and lasting learning, and facilitating learning have been highlighted, emphasizing their contributions to various disciplines. Quantitative studies have demonstrated that coding affects sequencing (Kazakoff & Bers, 2012 ; Kazakoff et al., 2013 ; Muñoz-Repiso & Caballero-González, 2019), problem-solving (Akyol-Altun, 2018 ; Bers et al., 2014 ; Çiftci & Bildiren, 2020 ; Fessakis et al., 2013 ), and executive functions (Di Lieto et al., 2017 ). Furthermore, coding and robotics education have significantly supported early mathematical reasoning skills in children (Blanchard et al., 2010 ; Caballero-Gonzalez et al., 2019; Di Lieto et al., 2017 ; Flannery et al., 2013; Kazakoff et al., 2013 ). Canbeldek and Işıkoğlu (2023) observed that coding and robotics education programs positively affected preschool children’s cognitive development, language skills, and creativity. Mısırlı and Komis (2014) found that their implemented program supported the development of mathematical concepts such as sequencing and repetition, algorithmic thinking, measurement, and spatial orientation in children.

Popat and Starkey ( 2019 ) highlighted those researchers mentioned that the inclusion of coding in school curricula provides a range of learning outcomes applicable beyond computer science. Meanwhile, Su et al. ( 2023 ) reviewed studies on coding in early childhood and emphasized that it is a new field focusing on imparting coding skills. The authors suggested evaluating the effects of coding curriculum on holistic learning outcomes in early childhood, such as school readiness skills (e.g., literacy, numeracy, spatial, and social skills). They emphasized the need to assess more critical child developmental outcomes like language, self-regulation, and metacognitive skills to understand the impact of coding curriculum. Zurnacı and Turan ( 2022 ) reviewed studies on coding in preschool education in Turkey, revealing that the most addressed topic was cognitive skills such as problem-solving abilities (in 7 studies), attention, sequencing, and analysis. The findings of this study also demonstrate an emphasis on the limited skills of cognitive development as a multidimensional process related to coding.

The study sought to address the question of “What are the contributions of using coding in early childhood education to the socio-emotional domain?” as the second sub-problem of the research. The study’s findings indicated that coding contributes to the socio-emotional domain by enhancing enjoyment, increasing motivation, fostering collaborative learning, improving communication skills, promoting personal development, empowering through increased motivation for responsibility, enhancing self-confidence, and facilitating focus. Bers ( 2008 , 2012 ), who studies coding in early childhood, states that children should be motivated while using technology and that working in a social and collaborative environment should support social and emotional skills along with these skills. Based on the positive youth development approach, he developed the PTG approach in programs and applications to be developed for children and applied this approach to his applications. In unplugged and block-based applications, he has drawn the framework of learning environments where children can be motivated while coding and develop their social skills by working collaboratively. He presented a road map to change the perspectives that technology negatively affects children’s social and emotional development and to support these areas of development.

Similar studies, like the results of this study, also indicate that coding supports socio-emotional development. Applications focused on coding demonstrate support for children’s peer collaboration, communication, and social relationships (Bers et al., 2019 ; Caballero-Gonzalez et al., 2019; Critten et al., 2022 ; Fessakis et al., 2013 ; Flannery et al., 2013; Lee et al., 2013 ; Sullivan & Bers, 2016 ; Pugnali et al., 2017 ). Studies have shown that coding supports children’s self-regulation skills (Canbeldek and Işıkoğlu, 2023; Di Lieto et al., 2017 ; Kazakoff, 2014 ). Heikkilä ( 2020 ) observed that robotics applications supporting coding generated significant interest in children, increased their patience and enthusiasm, and reduced gender-biased perspectives.

The study sought to address the question of “What are the comparisons of theses data and research articles data?” as the third sub-problem of the research. Theses data, which focus on LEGO-based education, primarily target elementary and middle school students, offering activities that foster creativity, problem-solving, and engineering skills. Students build structures, mechanisms, and robots using LEGO bricks, motors, and sensors. This approach benefits learners by developing their spatial reasoning and engineering abilities, although it can present challenges in the complexity of designs and motor programming. Teachers in this context typically serve as facilitators, guiding students through exploration and experimentation.

In contrast, the data of research articles revolves around robotics and coding education for preschool and early elementary school students. It emphasizes computational thinking, coding skills, and teamwork, often using tools like KIBO and Bee-Bot. Students participate in sequencing, programming, and interactive storytelling, which promote collaboration, critical thinking, and fine motor skills. However, integrating technology and ensuring age-appropriateness can be significant challenges for educators in this domain. Teachers play a more active role in designing activities and scaffolding learning experiences to suit the developmental needs of young learners.

While both topics aim to enhance students’ learning experiences and skills development, their target age groups, learning focuses, main tools, and teacher involvement differ. LEGO-based education leans towards older students and emphasizes hands-on building and engineering, while robotics and coding education cater to younger learners and prioritize computational thinking and programming skills. Despite these variances, both approaches contribute to fostering creativity, problem-solving, and critical thinking skills essential for success in the 21st century.

Due to the nature of meta-thematic research (Batdı, 2019 ), the data used in this study consisted only of articles and theses that presented experimental studies and direct participant views. Therefore, the comparison of articles and thesis studies was limited to these articles. A more detailed comparison is recommended to contribute to the field.

Reviews conducted on coding in early childhood (Lye & Koh, 2014 ; Macrides et al., 2022 ; Papadakis et al., 2016 ; Su et al., 2023 ) have revealed significant findings. These studies have indicated that intervention programs primarily focus on children’s coding and computational thinking skills, with a limited number examining their impact on developmental domains. The present study, however, has demonstrated an understanding of coding’s influence on cognitive and socio-emotional development. Furthermore, a significant finding of this study indicates a focus on a few foundational skills within cognitive and socio-emotional development through coding.

Previous review studies have contributed significantly to coding practices, approaches, methods, techniques, materials, and assessments used in these interventions. They have also outlined a framework for studies centered around coding. Additionally, it is believed that identifying views, thoughts, and trends in the field will provide substantial contributions from practitioners or researchers regarding their perspectives on coding, ultimately strengthening and enhancing studies.

This study suggests a trend indicating that coding contributes to cognitive and socio-emotional domains. However, coding is proposed to support various cognitive and socio-emotional development aspects. It is essential to empirically validate and confirm these views concerning the impacts of coding on development through empirical studies.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What is the difference. Future of Learning Group Publication , 5 (3), 438.

Google Scholar  

Akyol-Altun, C. (2018). Okul öncesi öğretim programına algoritma ve kodlama eğitimi entegrasyonunun öğrencilerin problem çözme becerisine etkisi (Master’s thesis), Ankara University, Ankara.

Arabacıoğlu, T., Bülbül, H. İ., & Filiz, A. (2007). Bilgisayar programlama öğretiminde yeni bir yaklaşım. Akademik Bilişim , 193–197.

Barendsen, E., & Stoker, I. (2013, November). Computational thinking in CS teaching materials: A pilot study. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research (pp. 199–200). https://doi.org/10.1145/2526968.2526995 .

Barron, B., Cayton-Hodges, G., Bofferding, L., Copple, C., Darling-Hammond, L., & Levine, M. H. (2011). Take a giant step: A blueprint for teaching young children in a digital age . Joan Ganz Cooney Center at Sesame Workshop.

Batdı, V. (2017). The effect of multiple intelligences on academic achievement: A meta-analytic and thematic study. Educational Sciences: Theory & Practice , 17 (6), 2057–2092.

Batdı, V. (2019). Meta tematik analiz örnek uygulamalar . Anı Yayıncılık.

Baytak, A., & Land, S. M. (2011). An investigation of the artifacts and process of constructing computers games about environmental science in a fifth grade classroom. Educational Technology Research and Development , 59 (6), 765–782.

Article   Google Scholar  

Bers, M. U. (2008). Civic identities, online technologies: From designing civics curriculum to supporting civic experiences . MacArthur Foundation Digital Media and Learning Initiative.

Bers, M. U. (2010). The TangibleK robotics program: Applied computational thinking for young children. Early Childhood Research & Practice , 12 (2), n2.

Bers, M. U. (2012). Designing digital experiences for positive youth development: From playpen to playground . OUP USA.

Bers, M. U. (2018a). Coding and computational thinking in early childhood: The impact of ScratchJr in Europe. European Journal of STEM Education , 3 (3), 8.

Bers, M. U. (2019). Coding as another language: A pedagogical approach for teaching computer science in early childhood. Journal of Computers in Education , 6 (4), 499–528. https://doi.org/10.1007/s40692-019-00147-3 .

Bers, M. U. (2020). Coding as a playground: Programming and computational thinking in the early childhood classroom . Routledge.

Bers, M. U. (2018b, April). Coding, playgrounds, and literacy in early childhood education: The development of KIBO robotics and ScratchJr. In 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 2094–2102). https://doi.org/10.1109/EDUCON.2018.8363498 .

Bers, M. U., & Horn, M. S. (2010). Tangible programming in early childhood. High-tech tots: Childhood in a Digital World , 49 , 49–70.

Bers, M. U., Ponte, I., Juelich, C., Viera, A., & Schenker, J. (2002). Teachers as designers: Integrating robotics in early childhood education. Information Technology in Childhood Education Annual , 2002 (1), 123–145.

Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education , 72 , 145–157. https://doi.org/10.1016/j.compedu.2013.10.020 .

Bers, M. U., González-González, C., & Armas–Torres, M. B. (2019). Coding as a playground: Promoting positive learning experiences in childhood classrooms. Computers & Education , 138 , 130–145. https://doi.org/10.1016/j.compedu.2019.04.013 .

Blanchard, S., Freiman, V., & Lirrete-Pitre, N. (2010). Strategies used by elementary schoolchildren solving robotics-based complex tasks: Innovative potential of technology. Procedia-Social and Behavioral Sciences , 2 (2), 2851–2857. https://doi.org/10.1016/j.sbspro.2010.03.427 .

Bocconi, S., Chioccariello, A., & Earp, J. (2018). The Nordic approach to introducing Computational Thinking and programming in compulsory education. Report prepared for the Nordic@ BETT2018 Steering Group , 42 .

Burke, Q., O’Byrne, W. I., & Kafai, Y. B. (2016). Computational participation: Understanding coding as an extension of literacy instruction. Journal of Adolescent & Adult Literacy , 59 (4), 371–375. https://doi.org/10.1002/jaal.496 .

Caballero-Gonzalez, Y. A., Muñoz-Repiso, A. G. V., & García-Holgado, A. (2019, October). Learning computational thinking and social skills development in young children through problem-solving with educational robotics. In Proceedings of the seventh international conference on technological ecosystems for enhancing Multiculturality (pp. 19–23). https://doi.org/10.1145/3362789.3362874 .

Campbell, C., & Walsh, C. (2017). Introducing the new digital literacy of coding in the early years. Practical Literacy , 22 (3), 10–12.

Canbeldek, M., & Isikoglu, N. (2023). Exploring the effects of productive children: Coding and robotics education program in early childhood education. Education and Information Technologies , 28 (3), 3359–3379. https://doi.org/10.1007/s10639-022-11315-x .

Ching, Y. H., Hsu, Y. C., & Baldwin, S. (2018). Developing computational thinking with educational technologies for young learners. TechTrends , 62 , 563–573. https://doi.org/10.1007/s11528-018-0292-7 .

Chou, P. N. (2020). Using ScratchJr to foster young children’s computational thinking competence: A case study in a third-grade computer class. Journal of Educational Computing Research , 58 (3), 570–595. https://doi.org/10.1177/0735633119872908 .

Çiftci, S., & Bildiren, A. (2020). The effect of coding courses on the cognitive abilities and problem-solving skills of preschool children. Computer Science Education , 30 (1), 3–21. https://doi.org/10.1080/08993408.2019.1696169 .

Clements, D. H. (1999). The future of educational computing research: The case of computer programming. Information Technology in Childhood Education Annual , 1999 (1), 147–179.

Clements, D. H., & Gullo, D. F. (1984). Effects of computer programming on young children’s cognition. Journal of Educational Psychology , 76 (6), 1051. https://doi.org/10.1037/0022-0663.76.6.1051 .

Clements, D. H., & Meredith, J. S. (1993). Research on Logo: Effects and efficacy. Journal of Computing in Childhood Education , 4 (4), 263–290.

Critten, V., Hagon, H., & Messer, D. (2022). Can preschool children learn programming and coding through guided play activities? A case study in computational thinking. Early Childhood Education Journal , 50 (6), 969–981. https://doi.org/10.1007/s10643-021-01236-8 .

CSTA, & K-12. (2020). Computer science standards. Computer Science Teachers Association , 12.

Czerkawski, B. (2015, October). Computational thinking in virtual learning environments. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1227–1231). Association for the Advancement of Computing in Education (AACE).

Demirer, V., & Sak, N. (2016). Programming education and new approaches around the world and in Turkey. Eğitimde Kuram Ve Uygulama , 12 (3), 521–546.

Di Lieto, M. C., Inguaggiato, E., Castro, E., Cecchi, F., Cioni, G., Dell’Omo, M., & Dario, P. (2017). Educational Robotics intervention on executive functions in preschool children: A pilot study. Computers in Human Behavior , 71 , 16–23. https://doi.org/10.1016/j.chb.2017.01.018 .

Digital News Asia (2015). IDA launches S $1.5 m pilot to roll out tech toys for preschoolers.

European Schoolnet (2015). Programming & coding policies: Where are we at European level? http://www.eun.org/news/detail?articleId=649724

Fesakis, G., & Serafeim, K. (2009). Influence of the familiarization with scratch on future teachers’ opinions and attitudes about programming and ICT in education. ACM SIGCSE Bulletin , 41 (3), 258–262. https://doi.org/10.1145/1595496.1562957 .

Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem-solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education , 63 , 87–97. https://doi.org/10.1016/j.compedu.2012.11.016 .

Flannery, L. P., Silverman, B., Kazakoff, E. R., Bers, M. U., Bontá, P., & Resnick, M. (2013, June). Designing ScratchJr: Support for early childhood learning through computer programming. In Proceedings of the 12th international conference on interaction design and children (pp. 1–10). https://doi.org/10.1145/2485760.2485785 .

Fox, R. W., & Farmer, M. E. (2011). The effect of computer programming education on the reasoning skills of high school students. In Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).

Futschek, G. (2006). Algorithmic thinking: the key for understanding computer science. In International conference on informatics in secondary schools-evolution and perspectives (pp. 159–168). Berlin, Heidelberg: Springer Berlin Heidelberg.

Futschek, G., & Moschitz, J. (2011). Learning algorithmic thinking with tangible objects eases transition to computer programming. In Informatics in Schools. Contributing to 21st Century Education: 5th International Conference on Informatics in Schools: Situation, Evolution and Perspectives, ISSEP 2011, Bratislava, Slovakia, October 26–29, 2011. Proceedings 5 (pp. 155–164). Springer Berlin Heidelberg.

García-Peñalvo, F. J., Rees, A. M., Hughes, J., Jormanainen, I., Toivonen, T., & Vermeersch, J. (2016, November). A survey of resources for introducing coding into schools. In Proceedings of the Fourth International Conference on Technological Ecosystems for enhancing multiculturality (pp. 19–26).

García-Valcárcel Muñoz-Repiso, A., & Caballero González, Y. A. (2017). Development of computational thinking and collaborative learning in kindergarten using programmable educational robots: A teacher training experience. https://doi.org/10.1145/3144826.3145353 .

Gibson, J. P. (2012). Teaching graph algorithms to children of all ages. In Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education (pp. 34–39). https://doi.org/10.1145/2325296.2325308 .

Goldenberg, E. P., & Carter, C. J. (2021). Programming as a language for young children to express and explore mathematics in school. British Journal of Educational Technology , 52 (3), 969–985. https://doi.org/10.1111/bjet.13080 .

González, M. R. (2015). Computational thinking test: Design guidelines and content validation. In EDULEARN15 Proceedings (pp. 2436–2444). IATED.

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher , 42 (1), 38–43. https://doi.org/10.3102/0013189X12463051 .

Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. Computer Science Education: Perspectives on Teaching and Learning in School , 19 (1), 19–38.

Heikkilä, M. (2020). What happens when the robot gets eyelashes? Gender perspective on programming in preschool. STEM Education across the Learning Continuum: Early Childhood to Senior Secondary , 29–44. https://doi.org/10.1007/978-981-15-2821-7_3 .

Heintz, F., Mannila, L., & Färnqvist, T. (2016). A review of models for introducing computational thinking, computer science, and computing in K–12 education. Teoksessa 2016 IEEE frontiers in education conference (FIE).

Hsu, T. C. (2019). A study of the readiness of implementing computational thinking in compulsory education in Taiwan. Computational Thinking Education , 295–314.

Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education , 82 , 263–279.

ISTE (2019). International Society for Technology in Education [Conference presentation]. Philadelphia.

Kafai, Y. B., & Burke, Q. (2014). Connected code: Why children need to learn programming . MIT Press.

Kalogiannakis, M., & Papadakis, S. (2017). Pre-service kindergarten teacher’s acceptance of ScratchJr as a tool for learning and teaching computational thinking and Science education.

Kazakoff, E. R. (2014). Cats in Space, Pigs that Race: Does self-regulation play a role when kindergartners learn to code? (Doctoral dissertation, Tufts University).

Kazakoff, E., & Bers, M. (2012). Programming in a robotics context in the kindergarten classroom: The impact on sequencing skills. Journal of Educational Multimedia and Hypermedia , 21 (4), 371–391.

Kazakoff, E. R., & Bers, M. U. (2014). Put your robot in, put your robot out: Sequencing through programming robots in early childhood. Journal of Educational Computing Research , 50 (4), 553–573. https://doi.org/10.2190/EC.50.4.f .

Kazakoff, E. R., Sullivan, A., & Bers, M. U. (2013). The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal , 41 , 245–255. https://doi.org/10.1007/s10643-012-0554-5 .

Lai, A. F., & Yang, S. M. (2011, September). The learning effect of visualized programming learning on 6 th graders’ problem solving and logical reasoning abilities. In 2011 International Conference on Electrical and Control Engineering (pp. 6940–6944). IEEE.

Lambert, L., & Guiffre, H. (2009). Computer science outreach in an elementary school. Journal of Computing Sciences in Colleges , 24 (3), 118–124.

Lee, F., & Björklund Larsen, L. (2019). How should we theorize algorithms? Five ideal types in analyzing algorithmic normativity’s. Big Data & Society , 6 (2), 2053951719867349. https://doi.org/10.1177/2053951719867349 .

Lee, J., & Junoh, J. (2019). Implementing unplugged coding activities in early childhood classrooms. Early Childhood Education Journal , 47 , 709–716. https://doi.org/10.1007/s10643-019-00967-z .

Lee, K. T., Sullivan, A., & Bers, M. U. (2013). Collaboration by design: Using robotics to foster social interaction in kindergarten. Computers in the Schools , 30 (3), 271–281. https://doi.org/10.1080/07380569.2013.805676 .

Lee, J., Jung, Y., & Park, H. (2017). Gender differences in computational thinking, creativity, and academic interest on elementary SW education. Journal of the Korean Association of Information Education , 21 (4), 381–391.

Lin, Y., & Weintrop, D. (2021). The landscape of Block-based programming: Characteristics of block-based environments and how they support the transition to text-based programming. Journal of Computer Languages , 67 , 101075. https://doi.org/10.1016/j.cola.2021.101075 .

Liu, H. P., Perera, S. M., & Klein, J. W. (2017). Using model-based learning to promote computational thinking education. Emerging research, practice, and policy on computational thinking , 153–172. https://doi.org/10.1007/978-3-319-52691-1_10 .

Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior , 41 , 51–61. https://doi.org/10.1016/j.chb.2014.09.012 .

Macrides, E., Miliou, O., & Angeli, C. (2022). Programming in early childhood education: A systematic review. International Journal of Child-Computer Interaction , 32 , 100396. https://doi.org/10.1016/j.ijcci.2021.100396 .

Mclennan, D. P. (2017). Creating coding stories and games. Teaching Young Children , 10 (3), 18–21.

McLennan, D. P. (2018). Code breaker: Increase creativity, remix assessment, and develop a class of coder ninjas. Journal of Teaching and Learning , 12 (1), 51–52.

Article   MathSciNet   Google Scholar  

Misirli, A., & Komis, V. (2014). Robotics and programming concepts in early Childhood Education: A conceptual framework for designing educational scenarios. Research on e-learning and ICT in education: Technological, pedagogical and instructional perspectives (pp. 99–118). Springer New York. https://doi.org/10.1007/978-1-4614-6501-0_8 .

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine , 151 (4), 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135 .

Monteiro, A. F., Miranda-Pinto, M., & Osório, A. J. (2021). Coding as literacy in preschool: A case study. Education Sciences , 11 (5), 198. https://doi.org/10.3390/educsci11050198 .

Moreno-León, J., Robles, G., & Román-González, M. (2015). Dr. Scratch: análisis automático de proyectos Scratch para evaluar y fomentar el pensamiento computacional. Revista de Educación a Distancia (RED) , (46).

Muñoz-Repiso, A. G. V., & González, Y. A. C. (2019). Robótica para desarrollar El pensamiento computacional en Educación Infantil. Comunicar: Revista Científica Iberoamericana De Comunicación Y Educación , 59 , 63–72.

National Association for the Education of Young Children (NAEYC) (2012). Technology and interactive media as tools in early childhood programs serving children from birth through age 8 (DRAFT). Retrieved from http://www.naeyc.org/content/technology-and-young-children .

New, R. S., & Cochran, M. (Eds.). (2007). Early childhood education: An international encyclopedia (Vol. 4). Greenwood Publishing Group.

Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2016). Developing fundamental programming concepts and computational thinking with ScratchJr in preschool education: A case study. International Journal of Mobile Learning and Organization , 10 (3), 187–202. https://doi.org/10.1504/IJMLO.2016.077867 .

Papert, S. (1980). Mindstorms Children . Computers and powerful ideas.

Papert, S. (2000). What is the big idea? Toward a pedagogy of idea power. IBM Systems Journal , 39 (3.4), 720–729.

Papert, S. (2005). Teaching children thinking. Contemporary Issues in Technology and Teacher Education , 5 (3), 353–365.

Pila, S., Aladé, F., Sheehan, K. J., Lauricella, A. R., & Wartella, E. A. (2019). Learning to code via tablet applications: An evaluation of daisy the Dinosaur and Kodable as learning tools for young children. Computers & Education , 128 , 52–62.

Popat, S., & Starkey, L. (2019). Learning to code or coding to learn? A systematic review. Computers & Education , 128 , 365–376. https://doi.org/10.1016/j.compedu.2018.10.005 .

Portelance, D. J., Strawhacker, A. L., & Bers, M. U. (2016). Constructing the ScratchJr programming language in the early childhood classroom. International Journal of Technology and Design Education , 26 , 489–504. https://doi.org/10.1007/s10798-015-9325-0 .

Pugnali, A., Sullivan, A., & Bers, M. U. (2017). The impact of user interface on young children’s computational thinking. Journal of Information Technology Education Innovations in Practice , 16 , 171.

Relkin, E., de Ruiter, L. E., & Bers, M. U. (2021). Learning to code and the acquisition of computational thinking by young children. Computers & Education , 169 , 104222. https://doi.org/10.1016/j.compedu.2021.104222 .

Resnick, M. (2006). Computer as paintbrush: Technology, play, and the creative society. Play learning: How play motivates and enhances children’s cognitive and social-emotional growth, 192–208.

Resnick, M., & Rusk, N. (2020). Coding at a crossroads. Communications of the ACM , 63 (11), 120–127.

Rushkoff, D. (2010). Program or be programmed: Ten commands for a digital age . Or Books.

Saxena, A., Lo, C. K., Hew, K. F., & Wong, G. K. W. (2020). Designing unplugged and plugged activities to cultivate computational thinking: An exploratory study in early childhood education. The Asia-Pacific Education Researcher , 29 (1), 55–66. https://doi.org/10.1007/s40299-019-00478-w .

Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies , 18 (2), 351–380.

Siper-Kabadayı, G. (2019). The effects of robotic activities on pre-school children’s creative thinking skills. (Master thesis). Hacettepe Unıversity, Ankara.

Strawhacker, A., & Bers, M. U. (2015). I want my robot to look for food: Comparing Kindergartner’s programming comprehension using tangible, graphic, and hybrid user interfaces. International Journal of Technology and Design Education , 25 , 293–319. https://doi.org/10.1007/s10798-014-9287-7 .

Strawhacker, A., Portelance, D., Lee, M., & Bers, M. U. (2015). Designing tools for developing minds: the role of child development in educational technology. In Proceedings of the 14th International Conference on Interaction Design and Children.

Su, J., Yang, W., & Li, H. (2023). A scoping review of studies on coding curriculum in early childhood: Investigating its design, implementation, and evaluation. Journal of Research in Childhood Education , 37 (2), 341–361.

Sulistyaningtyas, R. E., Yuliantoro, P., Astiyani, D., & Nugraheni, C. (2021, September). A Literature Review of Coding for Early Childhood In Proceedings of the 2nd Borobudur International Symposium on Humanities and Social Sciences, BIS-HSS 2020, November 18 2020, Magelang, Central Java, Indonesia.

Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: Learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education , 26 , 3–20. https://doi.org/10.1007/s10798-015-9304-5 .

Sullivan, A., & Bers, M. U. (2018). Dancing robots: Integrating art, music, and robotics in Singapore’s early childhood centers. International Journal of Technology and Design Education , 28 , 325–346. https://doi.org/10.1007/s10798-017-9397-0 .

Sullivan, A., & Bers, M. U. (2019). Computer science education in early childhood: The case of ScratchJr. Journal of Information Technology Education Innovations in Practice , 18 , 113.

Sullivan, A. A., Bers, M. U., & Mihm, C. (2017). Imagining, playing, and coding with KIBO: Using robotics to foster computational thinking in young children (p. 110). Siu-cheung KONG The Education University of Hong Kong.

Svensson, A. K. (2000). Computers in school: Socially isolating or a tool to promote collaboration? Journal of Educational Computing Research , 22 (4), 437–453. https://doi.org/10.2190/30KT-1VLX-FHTM-RCD6 .

Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology , 8 (1), 1–10. https://doi.org/10.1186/1471-2288-8-45 .

Toh, L. P. E., Causo, A., Tzuo, P. W., Chen, I. M., & Yeo, S. H. (2016). A review on the use of robots in education and young children. Journal of Educational Technology & Society , 19 (2), 148–163.

U.S. Department of Education, Office of Educational Technology (2010). Transforming American education: Learning powered by technology. U.S. Department of Education, Office of Educational Technology. http://www.ed.gov/technology/netp-2010 (Accessed 12 June 2023).

Van Roy, P., & Haridi, S. (2004). Concepts, techniques, and models of computer programming . MIT Press.

Vee, A. (2013). Understanding computer programming as a literacy. Literacy in Composition Studies , 1 (2), 42–64. http://d-scholarship.pitt.edu/id/eprint/21695 .

Vorderman, C. (2019). Computer coding for kids: A unique step-by-step visual guide, from binary code to building games . Dorling Kindersley Ltd.

Wang, D., Zhang, C., & Wang, H. (2011). T-Maze: A tangible programming tool for children. Proceedings of the 10th International Conference on Interaction Design and Children , 127–135. https://doi.org/10.1145/1999030.1999045 .

Wartella, E. A., & Jennings, N. (2000). Children and computers: New technology. Old concerns. The Future of Children , 31–43. https://doi.org/10.2307/1602688 .

Webb, M., Davis, N., Bell, T., Katz, Y. J., Reynolds, N., Chambers, D. P., & Sysło, M. M. (2017). Computer science in K-12 school curricula of the 2lst century: Why, what and when? Education and Information Technologies , 22 , 445–468. https://doi.org/10.1007/s10639-016-9493-x .

Wilson, C., Sudol, L. A., Stephenson, C., & Stehlik, M. (2010). Running on empty: The failure to teach k–12 computer science in the digital age . ACM.

Wing, J. M. (2006). Computational thinking. Communications of the ACM , 49 (3), 33–35. https://doi.org/10.1145/1118178.1118215 .

Zurnaci, B., & Turan, Z. (2022). Türkiye’de okul öncesinde kodlama eğitimine ilişkin yapılan çalışmaların incelenmesi. Kocaeli Üniversitesi Eğitim Dergisi , 5 (1), 258–286.

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Mehmet Başaran

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Başaran, M., Metin, Ş. & Vural, Ö.F. Meta-thematic synthesis of research on early childhood coding education: A comprehensive review. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12675-2

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Transcending technology boundaries and maintaining sense of community in virtual mental health peer support: a qualitative study with service providers and users

• Elmira Mirbahaeddin 1 &
• Samia Chreim 1  

BMC Health Services Research volume  24 , Article number:  510 ( 2024 ) Cite this article

Metrics details

This qualitative study explores the experiences of peer support workers (PSWs) and service users (or peers) during transition from in-person to virtual mental health services. During and following the COVID-19 pandemic, the need for accessible and community-based mental health support has become increasingly important. This research aims to understand how technological factors act as bridges and boundaries to mental health peer support services. In addition, the study explores whether and how a sense of community can be built or maintained among PSWs and peers in a virtual space when connections are mediated by technology. This research fills a gap in the literature by incorporating the perspectives of service users and underscores the potential of virtual peer support beyond pandemic conditions.

Data collection was conducted from a community organization that offers mental health peer support services. Semi-structured interviews were conducted with 13 employees and 27 service users. Thematic analysis was employed to identify key themes and synthesize a comprehensive understanding.

The findings highlight the mental health peer support needs that were met through virtual services, the manifestation of technology-based boundaries and the steps taken to remove some of these boundaries, and the strategies employed by the organization and its members to establish and maintain a sense of community in a virtual environment marked by physical distancing and technology-mediated interrelations. The findings also reveal the importance of providing hybrid services consisting of a mixture of in person and virtual mental health support to reach a broad spectrum of service users.

Conclusions

The study contributes to the ongoing efforts to enhance community mental health services and support in the virtual realm. It shows the importance of virtual peer support in situations where in-person support is not accessible. A hybrid model combining virtual and in-person mental health support services is recommended for better accessibility to mental health support services. Moreover, the importance of organizational support and of equitable resource allocation to overcome service boundaries are discussed.

Peer Review reports

There is growing awareness around the world of the need to improve mental health services, yet the response to the need has been constrained [ 1 ]. The World Health Organization (WHO) has pointed to the urgent need to invest in community-based mental health services that prioritize a person-centred, recovery approach. Among these services, the WHO highlights the importance of peer support [ 1 ]. Formal mental health peer support refers to emotional and social support (Mental Health Commission of Canada, https://mentalhealthcommission.ca/what-we-do/access/peer-support/ ) provided by an individual referred to as a peer support worker (PSW). A mental health PSW is a person who has lived experience of mental health issues, has paid employment in a mental health support or services organization– often after receiving training– and offers intentional support to clients with mental health challenges through empathetic understanding and encouragement of self-determined recovery [ 2 , 3 ].

Peer support is based on the belief that individuals who have navigated their own recovery experiences hold unique insights and lived practical knowledge that can be helpful in supporting others in their recovery (Mead, Hilton & Curtis, 2001). The notion of recovery in mental health refers to a multidimensional process that involves individuals actively engaging in their own well-being, making self-determined choices, fostering social connections, and pursuing a meaningful life despite the presence of mental health challenges (Mead, Hilton & Curtis, 2001). Peer support represents a political alternative to professionally led services and decision-making processes; it is an important approach for promoting the agency of individuals with mental health issues and reversing the power imbalances prevalent in the mental health system. Peer support can promote empowerment and self-efficacy, help enhance coping skills and strategies, and contribute to overall quality of life and emotional well-being [ 4 , 5 , 6 ]. It has been particularly helpful in situations where traditional professional mental health services might not fully address the needs of individuals or are not easily accessible [ 3 , 7 ].

The importance of peer support became particularly salient during the COVID-19 pandemic. The pandemic adversely affected access to in-person mental health services, especially in jurisdictions where lockdowns were enacted. Peer support services in an online format created an opportunity to maintain availability and accessibility to basic yet important community-based mental health support [ 8 ]. A number of jurisdictions increased their peer support capacities by offering PSW training on remote services during the COVID-19 crisis (e.g., the Digital Peer Support Certification for peer specialists in the US that provided Medicaid-reimbursable virtual health services) [ 9 ]. Virtual peer support services have been beneficial in various ways including overcoming geographical barriers, reducing regional inequalities in access to providers, and offering convenience for a wide range of vulnerable populations in communities [ 10 – 11 ]. Hence virtual peer support has created bridges allowing people in need of mental health support to access it. These bridges can be advantageous not only in crisis situations such as the pandemic but also in non-crisis contexts by offering expanded accessibility.

There has been growing use of technology for a variety of mental health and support services with an aim to improve accessibility [ 10 , 11 , 12 ]. However, the move to provide mental health services and support remotely, despite its many benefits, also comes with challenges. These challenges include, among others, the need for providers and service users to adapt to the utilization of diverse technologies including synchronous (e.g. video calls) and asynchronous (e.g. apps) modalities [ 11 ]. We view the technological challenges as setting boundaries to providing, accessing and utilizing virtual services.

Existing literature does not provide adequate insight into how individuals adapt when a sudden and major change occurs from in-person to remote mental health and support services. Makarius & Larson (2017) state that the role of individuals in virtual work has been overlooked by considering them as “passive actors” [ 13 , p.166] while portraying organizations as accountable for effective virtual work. They indicate that extant research on virtual work has tended to focus on virtual teams. Therefore, there is a need for a greater focus on individual experiences [ 14 – 15 ]. This applies in a general sense, but also, specifically to peer support. With the advent of COVID-19, PSWs became one of the forefront providers of mental health support [ 9 ]. Service users also had to adjust to virtual services. Yet limited knowledge exists about the individual experiences in the process of adapting and acclimating to using online mediums in virtual services in the context of peer support [ 16 ]. As virtual mental health services and supports are expected to continue to be used in the future, the experiences of individuals providing and receiving virtual peer support have become an important research topic.

Another issue of importance that needs to be considered when peer support is delivered virtually is whether technology-mediated connections allow peer support groups and individuals to maintain a sense of community. This sense of community is grounded in people’s relationship with a group that offers them membership, fulfillment of needs, and shared emotional connection [ 17 ], yet it is unclear whether the sense of community that is characteristic of in-person peer support is severed when services move online.

Earlier conceptualizations of communities emphasized the spatial dimension, defining communities as groups of people associated with a setting such as a neighbourhood or village [ 18 ]. McMillan and Chavis (1986) point to earlier work [ 19 ] that distinguished between the geographical notion of community (such as a neighbourhood or town) and the relational notion concerned with human relationships regardless of location [ 20 ]. McMillan and Chavis [ 20 , p. 9] propose a definition of sense of community that applies to both of these conceptualizations, and is as follows: “Sense of community is a feeling that members have of belonging, a feeling that members matter to one another and to the group, and a shared faith that members’ needs will be met through their commitment to be together.” These authors point to four elements in their definition: (a) membership (a feeling of belonging or personal relatedness), (b) influence (a sense of mattering to the group), (c) integration and fulfillment of needs (a feeling that needs will be met through membership in the group), and (d) shared emotional connection (a belief that members have shared history and similar experiences) [ 20 , p. 9].

In peer support communities, the principles of valuing individuals’ experiential knowledge of mental illnesses, determination for recovery, equality and reciprocity, and mutual agreement on what would be helpful for different individuals play a vital role [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. People benefit in different ways by having a sense of community. They experience less isolation and social exclusion, have a greater sense of well-being, can call on support when they need it and learn from the experiences of other members [ 22 – 23 ]. Cronenwett & Norris (2009) examined the role of social collectives in providing peer support services to individuals with co-occurring disorders and the benefits of social support and shared experiences in promoting recovery [ 24 ]. However, it is not clear yet how peer support sense of community is created or maintained in situations where peer support moves to a virtual space and relationships are mediated by technological tools. To our knowledge, this topic has not been addressed despite its importance.

Given the importance of peer support and the recent surge in virtual peer support service provision, our objective is to understand how technological factors can act as bridges and boundaries to services, and whether and how a peer sense of community can be built or maintained in a virtual space that relies extensively on the use of technological tools. We aim to understand these issues from the perspective of individuals affected directly by the changes from in-person to virtual services. Therefore, we focus on PSWs who provide support services, and on the service users or clients– also known as peers. Inclusion of peer voices is particularly important, given that this is a gap in the literature since much research on peer support is based on the views of managers and PSWs, and not on the views of the peers themselves [ 25 ]. This limitation in the literature applies to peer support specifically, but also more broadly. For example, a systematic review investigating the implementation and adoption of telemental health found that research studies involved fewer service users compared to the number of providers (only 9 out of 45 included papers involved service users), indicating that the point of view of service users has not been adequately researched and little is reported about their experiences [ 26 ].

Hence, we ask the following research questions: What mental health peer support needs were met with virtual services? How were technology-based boundaries manifested and what bridges were built to open boundaries? How, if at all, was a sense of community established or maintained in a virtual space? We researched these topics in the case of a peer support organization that transitioned from in-person to virtual services during the COVID-19 pandemic. While in the case we studied the move to a virtual space was a response to exacerbated mental health challenges during the pandemic, it also opened up opportunities to understand if and how peer support could be enacted virtually beyond pandemic conditions . The surging interest in providing mental health services and support virtually thus makes our study a timely endeavor, and our findings a valuable addition to the literature.

Study design and context

We adopted an exploratory case study approach [ 27 ] as it allows us to understand complex social phenomena and generate new insights [ 28 ]. We aimed to achieve a deep understanding of how members of a peer support organization viewed or experienced mental health needs within the broader social context of the pandemic, how they interacted with technological aspects of virtual services, and the strategies they used to create a sense of community in a virtual space.

Our primary data consisted of semi-structured interviews with employees (PSWs and/or managers) and service users (or peers) of a peer support organization based in a major city in Ontario, Canada. This organization had more than twenty compensated PSWs, some of whom held managerial positions in the organization. It served the needs of a large number of peers who sought its various services. Before the COVID-19 pandemic, this organization primarily offered in-person services that included, among others, various peer groups as well as recreational and social programs which were also intended to provide support. We initiated the data collection in the early stages of the pandemic when lockdown regulations were implemented in Ontario. The reason for selecting this particular case was the organization’s rapid transition to virtual platforms in response to increased demand for peer support during lockdowns and isolation.

Data collection

We collaborated with the organization in informing potential participants about the study. An email was sent by the organization to all its employees and service users informing them about the study, and inviting individuals interested in participating to contact the researchers. Thirteen PSWs and twenty-seven service users contacted the researchers. We interviewed all individuals who contacted us, thus our study included forty participants. Participants’ age ranged between being in their 20s and 60s, and the majority identified as female.

We conducted semi-structured interviews with participants. Different interview protocols were developed for each group of participants, and they were developed for this specific study. Based on the research questions and objectives, key themes were identified to guide the formulation of the interview questions. Moreover, the interview protocol was informed by existing literature on mental health peer support, the pandemic circumstances and concepts relating to boundary theory and sense of community. We adjusted the interview questions to account for feedback from the organization, whose approval we sought on the final interview protocols. A small group representing managers, PSWs and peers participated in providing feedback and validating the interview protocols. Overall, the questions were crafted to be clear and open-ended to encourage detailed responses and in-depth exploration of the subject matter. The interview protocols included questions on individuals’ mental health experiences during the pandemic, their experiences associated with opportunities and challenges of virtual services technology, the strategies that they and the organization used to capitalize on opportunities, remove difficulties, and build or maintain a sense of community. Open-ended questions enabled us to probe for additional details and allowed the participants to share beyond our questions, which provided us with rich and nuanced data [ 29 ]. The interviews were conducted via Zoom or phone, based on the participant’s preference. The interviews were conducted during the pandemic from February to November 2021. They were recorded and transcribed verbatim.

Data analysis

We conducted thematic analysis and used the N-Vivo software for data coding and retrieval. Specifically, we followed the steps outlined by Braun and Clarke (2006) [ 30 ]. Familiarization with the data started with both authors conducting a number of interviews conjointly, taking notes during this process and discussing the preliminary data. Familiarization was enhanced by the first author’s transcription of the interviews. We then generated initial codes by immersing ourselves in the data. The long list of initial codes– or descriptive codes [ 28 ]– was closely related to participants’ words. We then identified emergent themes by grouping similar codes together and reviewing that the coded extracts fit the themes. The process involved constant comparison and was iterative in that we reviewed the codes and themes and changed the theme names when we identified emergent ideas based on new data. Analysis was mostly inductive, but we had also been sensitized by extant literature. In the later stage of the analysis, we grouped the themes into more abstract categories, continuously reviewing and refining the categories. Our final descriptive codes and theme list is presented in Table  1 .

The first author performed the primary analysis and the second author reviewed the analysis on the basis of the data. When the authors’ interpretations differed, they returned to the data to find answers. This process offered confidence that the analysis was well anchored in the data from participants. We conducted member checking—explained in the next section— by seeking feedback from the participants on our analysis.

Establishing trustworthiness

We took several steps to establish the trustworthiness of the study [ 28 , 31 ]. Two researchers worked together on data analysis, returning to the data when disagreements emerged. This offered triangulation through the involvement of two researchers. We also report extensive quotes from our participants as evidence of our analysis. In addition, we conducted member checking to determine whether our findings captured well the experiences of participants and thus ensured the credibility of the results. This entailed sharing a draft of the manuscript with the participants and asking them to provide their feedback on the researchers’ interpretation and whether those aligned with their experiences. We received feedback from two PSWs and five peers, all of whom were in agreement with the results reported. One participant commented, “ I feel that the paper captured… challenges and victories peer supporters experienced during COVID ” and another participant stated, “ It is a good in-depth work/story showing the mental health challenges and how those were addressed during the pandemic, how people evolved from their experience and stood for each other when it mattered the most. ”

Research ethics

The study was approved by the Research Ethics Board (REB) of the University of Ottawa (Reference number S-11-20-6226). All study participants were fully informed about the project through both written and oral communication, and willingly gave their consent. The consent form included information about mental health resources available to them if needed, and participants were informed about their right to withdraw from the study. All procedures followed the appropriate guidelines and regulations.

We begin with the results showing the need for virtual mental health support during the pandemic and follow with the technology-based boundaries and bridges identified in virtual mental health support. In the last section of the results, we focus on the strategies that were used by the collective to maintain a sense of community despite the physical distances. It is important to note that we give attention to pandemic-related dynamics where pertinent, but also go beyond the pandemic context to address more general issues related to virtual peer support that were central in our participants’ accounts.

Need for virtual mental health support services

Boundaries related to accessing in-person services.

The pandemic amplified social issues that resulted in a surge in mental health challenges. Peers shared concerns regarding social vulnerabilities that became exacerbated during the pandemic. They told us about their challenges which included homelessness, domestic abuse, and struggles with addiction that were exacerbated during lockdowns. One peer referred to the “ downward spiral [of mental health] once the COVID-19 pandemic hit”. A peer pointed out that “literally everything shut down in the city…the needs of the community are just desperate ”, and a PSW stated that “with the pandemic, there was a lot of isolation, and it was really hard…also just the transition back as things started opening up. It’s really anxiety provoking for a lot of people. ”

There was also difficulty finding mental health services as there were lengthy wait times to see a mental health professional. A peer stated: “I think the most difficult thing was probably finding people to connect with…. There was a three-month waiting list to be able to even speak to anybody.” It is important to note that accessing mental health services in person was difficult for many people even before and regardless of the pandemic. The following quote by a peer illustrates one of many situations under which accessing in-person peer support can be difficult: “When you have a baby, it’s hard to be somewhere on time and remember to bring everything that you need and deal with the cranky baby… When your expectation is that you’re going to participate in these types of groups in-person, it can be very jarring ”.

Virtual peer support as a bridge

Virtual services can be a bridge connecting individuals to mental health peer support, especially when these individuals experience challenges with attending in-person peer activities. The peer who reflected above on the difficulties associated with accessing in-person peer support pointed out that “ when you can proceed in groups virtually, you can mute yourself, you can step away, your baby’s crib is right there…. So it was a really wonderful option.” A peer reflecting on the high cost of seeking “formal therapy” and the inconvenience of doing so, pointed out that virtual peer support was “a light in the tunnel” for them: “it was free, it was accessible, it was easier to find a peer support group during times that I could access it. During the pandemic, I accessed more groups than I did [in person].”

We also heard from peers whose anxieties had been exacerbated during the pandemic. A peer shared that seeking in-person mental health support was a major challenge. This person added that “ it was nice to be able to access things from Zoom”. Considering the risk of contracting the coronavirus, peers felt that not having to leave the house gave them a “sense of accomplishment” because accessing services remotely helped them remain engaged. A peer noted that virtual peer support had been “ the winter month survival ” for many individuals.

Peers also told us that virtual support was helpful for them in general, and not only because of the pandemic. Social anxieties, unrelated to the pandemic, were often mentioned by participants. A peer stated: I’m very timid to talk in a support group, and with Zoom, I feel I can raise my hand with the computer and I get to speak. Whereas in a peer support group in person, I don’t always get to do that. And… you get to see everybody’s facial reactions when you’re in the gallery view [on Zoom], whereas you can’t do that when you’re in the group because I’m very shy and very anxious.”

Anxieties were also related to driving. A peer stated, “I feel grateful I don’t have to drive far or pay for parking. Without the anxiety of driving and being on time too is very relaxing… (Virtual peer support) is a blessing.” For some individuals for whom transportation to in-person meetings could be difficult due to time or financial constraints, virtual services opened the possibility of receiving peer support.

Peers also told us how the virtual services facilitated receiving support in cases where struggles with depression kept them from seeking in-person services: “ If you’re so depressed, it’s hard to get out of bed… That’s another thing about Zoom, you don’t have to worry so much about your appearance. If you haven’t washed your hair that day, it’s fine… It makes it so much easier to attend .”. Virtual services were also very helpful for peers who felt they needed to seek support frequently: “ I’ve struggled with feeling alone and… feeling overwhelmed… If I had to go to a walk-in, I wouldn’t have done it. I wouldn’t have had access and that would have been bad for me .” Moreover, peers who felt self-conscious about their appearance, had experienced weight shaming, or physical differences found it more comfortable to attend virtual meetings because they “take away the self-consciousness” as a participant stated. By allowing participants to control what they reveal (e.g. by turning the camera on or off), virtual meetings may offer a certain sense of safety that in-person meetings may not provide.

Importantly, we were told that new members had joined virtual meetings who had not previously participated in in-person peer services. A manager pointed out that “a lot of new people who were not previous members have joined the community to get support or to get social interaction” and a PSW stated: “ we are supporting more people now. Our meetings are much larger. I’ve had people contact me from other provinces asking ‘Am I allowed to join?’ We’ve decided that as long as we have the capacity, anybody who wants can come .”

In sum, virtual services offered benefits for individuals who struggled with various issues including anxieties and depression, or whose life circumstances made it difficult for them to commute to in-person meetings. Although the pandemic (and the lockdowns associated with it) exacerbated some of the challenges that people had faced, the quotes above indicate that some challenges were not specifically pandemic-related, but rather pertained to more general mental states and life circumstances. The fact that virtual meetings drew in attendance from individuals who had never been to in-person meetings is a further indication that virtual platforms increase accessibility for peers.

Boundaries and bridges relating to telecommunication technology for virtual mental health support

Accessing virtual services offered peers opportunities to receive support, but accessing these services had its own challenges. A major challenge was technology, which manifested in terms of access to and compatibility of devices, access to internet connection, and basic technological skills. We report on these challenges and on how they were mitigated.

Virtual service technology boundaries

Technology-based challenges were associated with access to and use of equipment, access to internet connections, and limited technology-based skills. Some individuals from both groups (peers and PSWs) found it difficult to transition to virtual services due to the unprecedented complexities introduced by the new service environment: “ the hardest thing for people is the technology part of it .” The experience of change to virtual services was described as “ anxiety-provoking ” for people who were not familiar with the use of technology such as computers and smartphones in daily life.

Accessing virtual services required the use of the appropriate equipment such as smart phones, and for some peers, access to these devices was a challenge. A peer described: “ The devices that I had access to were lower-end devices… My cell phone was blocking out and freezing ”. Another peer stated: “ I would drop in occasionally using my phone. But I didn’t have a computer, and currently, I’m receiving disability benefits… As far as having money to burn, that’s not an option for me, it’s a very tight situation ”. In addition, lack of access to and reliable internet connection was another boundary. A participant described the lockdown situation: “ It was a big shock. It’s a big change. It’s forcing a lot of people who didn’t have the Internet to get Internet. So that caused a lot of stress and strain on a lot of people ”. Peers who shared an internet connection with multiple residents had to coordinate schedules since simultaneous Zoom calls could interrupt connections.

For some individuals, a lack of technology-based skills was a boundary. Some peers had difficulty navigating the nuances of the various platforms and their compatibility with the devices they were using: “ You had to figure out what platform was used and whether or not your technology was going to be compatible with it. ” Other peers experienced difficulties early on with logging in and accessing meetings: “[It was a] struggle with the process of getting signed up, to get the notifications, to get the information ”. Others reported difficulty navigating the programs’ options during the meetings (e.g., using the raise hand option). The challenges did not only pertain to peers. PSWs also faced difficulties with technology: “I did not have the technology needed to be able to do my job from home. I had a smartphone, but it’s still very challenging to host a Zoom group when I can only see 4 little faces on a screen.”

Virtual service bridges: supports provided by the organization and PSWs

When the lockdowns were mandated, concern about peers’ mental health needs drove the organization to create a variety of platforms through which peer support services could be accessed. Within a few weeks, the organization created remote services to maintain continuity in support for peers. A PSW pointed out “They were relying on us for their well-being.” This created a sense of urgency to adapt quickly in order to meet the needs of the community.

Efforts were quickly deployed to connect with peers by phone and to create accessibility through online options. As a peer stated, they were “ trying to make things just as accessible as they could be ”. To this end, the organization engaged in advocacy efforts with external partners to provide devices, data, and internet connection to those without technology. A manager stated: “Many people with mental health and addictions don’t even have access… We have been providing people with tech and tablets and smartphones and connectivity, and we’re a peer agency, we don’t have this kind of stuff!… I kept raising it at our (regional health authority) table with a lot of people who are very high up. And they said, ‘Let’s do it’! So we applied and put together a proposal… We now have contracts with [internet] providers, so [one company] provides the smartphones with sim cards and [another company] provides the tablets.”

PSWs walked peers step by step through the Zoom functions that they needed in order to attend and participate in virtual meetings. A PSW pointed out: “ We did a lot of one-on-one training and coaching and mentoring with people to help them get their virtual equipment set up. At first, it was a lot of, ‘this is how you set up Zoom, this is how you set up your camera’… and then more people got comfortable using it .”

PSWs also received training and support. Training included group and one-on-one sessions, and manuals were made available to provide instructions for an online environment: “In the beginning, we had training from a staff member who is a certified online facilitator… and it walked us through how to use Zoom. I also had one-on-one training… to walk me individually before doing any online groups… I asked my questions, and felt comfortable then to roll with it, [and] manuals were written with the policies of how we were gonna do this online.”

The social media team of the organization also became very active during the early days of the lockdowns. A manager who was part of this team described the role of the social media team: “We re-did all the posters we had for in-person, we switched them to virtual, giving new contact information, laying out the registration process…Every day we posted what groups we had going on, and all of that content had been created after the pandemic started. Again, a lot of that very quick adaptation to the needs.” We were also told that the organization added and adjusted online group activities and services as the lockdown policies and the needs of the peer community changed.

In sum, the findings show the challenges and solutions relating to using telecommunication technology for virtual mental health support during the COVID-19 pandemic. Accessing and providing these virtual services required access to and compatibility with devices, reliable internet connection, and technology-based skills, which could be challenging for some individuals. To address these challenging access boundaries, the organization arranged to provide devices, data, and internet connections, along with training and ongoing support to both peers and PSWs. Meanwhile, the organization also experienced a learning curve as it was adapting to the new circumstances and applied efforts to bridge the gaps in service access.

Maintaining a sense of community in virtual mental health support services

The peer support community already existed before the pandemic lockdowns. Peers would come to the organization locale for in-person services and programs, and many relied on these programs for mental health support. The lockdowns were disruptive of the in-person programs, which had to be halted, and as we elaborated earlier, the organization quickly responded by establishing services online. We were interested in whether and how a sense of community could be re-established and maintained in a virtual environment. Our findings point to five strategies in which the organization and the peers engaged, and which enabled maintaining a sense of community. We present these strategies next, and would like to point out that although we discuss them separately to facilitate the presentation, these strategies were not mutually exclusive.

Maintaining continuous presence and social interaction

In a context of increasing isolation, and to meet the needs of peers, the organization quickly began to offer phone services whereby peers and PSWs could connect by phone. Participants told us the phone support communicated a sense of caring and had a significant impact on individuals’ mental health during the pandemic. One of several volunteer peers who took on the task of checking on other peers regularly, indicated that for some individuals, their only connection to the outside world was through these phone calls: “ It could mean the difference between being stable and unstable… Being unstable for a long time could lead to something terrible .” Phone calls were not only about mental health topics, but could also include friendly conversations about daily living activities, which solidified relationships. The peers looked forward to these phone calls as a means of getting positive contact with someone who cared to listen. As one peer said, “They opened up a phone line and… I would call almost every day… I really needed [peer support]… So having that as a service was really, really good.” And another peer stated: “[It was great] knowing that they’re always there. It’s just the comfort of knowing there’s someone to reach out to. ”

It is important to note the speed with which the organization was able to adapt and to create programs that met the peers’ needs, thus maintaining a continuous presence. As a manager stated, “ [peer support] works well in a pandemic because we were able to be more flexible.” This is in contrast to institutional mental health services that were subject to various regulatory restrictions that would delay the introduction of online services. A PSW stated, “ we are extremely adaptable.”

In short order, the organization created a variety of online groups and activities in which peers could register and participate. These programs allowed the peers to continue interacting and engaging with one another. The sense of community was palpable even for peers who did not participate actively in the programs: “So for these people [like me], even though their videos and microphones are off, being immersed in the group, feeling like, hey, I’m not the only one, these are my people… and they look good and they’re talking and they’re feeling great. I feel good being there. And I may not want to say anything. It’s amazing. It’s a good feeling.”

Another peer commented on the relationships with the PSWs in the virtual meetings and said “… you can access [virtual support] anywhere and see the facilitators that you’re connected to. And that sometimes is enough to just make my spirit go fly. ” A similar sentiment was communicated by PSWs, one of whom stated: “We have things seven days a week that peers can come and join us. That has been really great; [it] helps keep the sense of community because we have that touchpoint with them. “

Establishing multiple points of connection

The organization was intent on meeting the diverse needs of peers, and to this end, created a variety of virtual programs and groups as well as phone services. In addition to the mental support groups, there were special activities such as yoga, crafting, and cooking, all of which instigated mutual support. These various activities could draw in diverse people who share similar interests, creating online communities. Peers stated that despite the lack of one-on-one eye contact, they found online groups were effective in offering valuable social activities related to wellness, nutrition, parenting, and gender-based support. One peer noted, “ They have a variety (of services)… Sometimes I’m in the mode of meeting [people], or joining arts and crafts. Sometimes I join the trivia online.” Another peer indicated that it was possible “to find the niche of the thing that you were looking for ” and a third peer stated: “ the trivia for me is very engaging… everybody can play. ”

The availability of multiple points of connection implied that the peers and PSWs could remain connected to each other on a regular basis. Another initiative by the organization to encourage this sense of community was the creation of a Facebook group. Due to the variety of points of contact, new members joined as they learned about the virtual services, expanding the community. However, the main aim of the organization remained to continue providing mental health support. A manager stated: “A lot of what people wanted was social connection, which we do offer in recreation. But we’re a support-based organization, and even our recreation has some support components to it. We came up with this private Facebook group which has helped a lot with that because people can stay in touch, not just with facilitators or with a group in a moment, but they can talk to each other whenever they want should they choose to join. “

Building on organizational and peer culture

Participants pointed out that peer culture is permeated by care and concern for members, and this was clear in various quotes we reported above from managers, PSWs and peers. In fact, managers and PSWs are also peers and they pointed this out continuously during our study. For example, a manager stated: “ It’s very helpful when peer support is informed by a community of people. And when peers can run some of their own services and see that peers are not only people who are recipients of services but actually are also managers ”. This manager also pointed out: “A peer-run community of peer supporters can help people meet different needs: their creative needs, their social needs, their support needs. There are physical needs, we’re doing some walking. We’re supporting people to get technology so they can not only take part in our Zoom meetings but also order their own groceries online or maybe they can talk to their doctor online now. Peer support has a lot of strengths.”

Another manager noted, “ It’s never just a job for people [at the organization]. It’s about how we can create something that is going to benefit the people who need it .” This focus on helping and supporting each other was integral to the organization’s mission and culture. This focus was shared by peers. Increased involvement of peer volunteers, who were not paid by the organization, in running services including the voluntary phone line was highlighted as an example of peer values and practices. A manager explained, “ One of the things that’s really important is to rely on the people who are actually DOING the thing, as opposed to me saying “well I know what’s good for this”, but actually leaning into our values .” Various participants mentioned that the implementation of online mental health support during the pandemic was an indication of resiliency in the peer support community. A peer stated “ We weren’t able to meet face to face. So people took it upon themselves to set up and organize these meetings and to learn how to use the technology to provide those services. ”

Acting collectively

The sense of community was also enabled by how decisions were made in the organization and with the help of peers. Deciding and acting collectively helped maintain a sense of community in the virtual space. This approach was especially effective during times of disruption that affected the organization and the peers. Overall, the organization’s collaborative approach to decision-making and focus on benefiting those in need were key components of its success.

The organization relied on discussion-based decision-making, with all staff members coming together weekly to discuss various issues and make decisions for the week. The management approach was collaborative and non-hierarchical. A manager said, “ We make decisions with the management collectively, and at times, when it’s appropriate, we make decisions with all staff .” Another manager described how “ the hierarchy felt a lot flatter” during the pandemic and the priority became “Who’s got what competencies? Who’s got what skills? Bring them in!” . Different members of the organization contributed their knowledge and skills to enhance the capacity to move services online. A PSW said: “We all bring our own perspectives. So I said my specialty is looking at the programming and the scheduling and what is feasible for us as staff… it was a lot of communication.”

Sharing lived experiences and learning together

Peer support is based on the shared lived experience of individuals. Sharing these experiences helps build bonds among peers. We were interested in how the virtual environment could have affected the sharing of experiences. Although some peers pointed out that they found it easier to share experiences in person, others– as we showed earlier– indicated that the online environment made it easier for them to participate. A PSW indicated: “We offer that space to just connect… Even though we’re saying “You gotta raise your hand before you talk”– that was an adjustment period. But now it’s the norm… That sense of belonging comes from connecting around shared lived experiences. So connecting around that shared lived experience is still happening. It’s just virtual, and a little more systematic.”

A peer described how the shared lived experience was helpful when using virtual services during the pandemic: “The ability to participate with other people who are struggling [was helpful], I just think that sharing those feelings and hearing that you’re not alone was worthwhile to me ”. Another peer reflected on the importance of the virtual services for connection around shared experiences of feeling “lost”: “It was a wonderful place to connect with people who were also struggling when everybody was sort of lost and in the same boat”.

Shared experiences were not limited to feelings of being lost and struggling. Members were also learning together, which solidified the sense of community. A peer pointed out: “[Relationships] became stronger in a sense, because we were all in the same boat… Sometimes the facilitators themselves were like I don’t know how to do that . We were all learning…and figuring things out. And I think that’s a good way to become closer to people. ”

In sum, various strategies were used by the organization and the collective (including PSWs and peers) to build and maintain a sense of community that was anchored in peer culture values.

Continuation of mental health support through a hybrid mode: importance of combining in-person and virtual services

Virtual peer services were “a lifeline” especially during the pandemic, as a peer noted. However, some peers also looked forward to returning to in-person services for various reasons. For some, the in-person services provided structure to their week and a chance to leave the house. A peer noted: “It forces me to get out of the house…I’m having difficulty leaving the house…half of me looks forward to it [the weekly support meeting], and half of me dreads it. But in the end, I get myself out of the door and I walk up to the center…I feel so much better afterwards.”

Naturally occurring conversations during coffee breaks or after the meetings, which contribute to supporting relationships, were missed. As one peer stated, “ A lot of it [peer support] is the action piece and when you’re connecting virtually, it’s just not the same as being in person ”. Some participants pointed out that in-person interactions offered a deeper level of connection through shared energy and physical space. A participant noted, “ When someone’s super upset, you can feel it. When people are in their own homes, it feels disconnected because there are so many other people there. I feel like we’re seeing less emotional distress, whereas in-person, it would be brought out– and not distress in the sense that they’re not coping, but that they’re bringing big feelings or things on their mind and they’re expressing them freely in person. I feel there’s a lot less of that since being virtual .” Additionally, some participants felt “strange” expressing strong emotions through a computer screen and pointed out that virtual settings offered less authentic connections compared to in-person interactions. Nonetheless, participants acknowledged that some people could still struggle regardless of the mode of interaction.

It was also pointed out that although virtual events drew in people who had never attended in person, some peers who used to attend in-person meetings did not join any virtual meetings, and it was not clear why this was the case or how they coped with the pandemic. Some of these individuals could not be found on online platforms to connect with. A participant stated, “… there’s a whole voice of those who can’t access virtual, those who have only been going in-person… So I think we definitely should try to cater to both [when designing mental health support services ]”.

Overall, peers expressed support for maintaining remote online mental health peer support services even as lockdowns were lifted, and pointed out that transitioning to a hybrid mode would offer efficiency in resource utilization and greater convenience for remote access. A peer emphasizing the need to continue the virtual services noted the importance of social integration for peers with disabilities: “ I think there’s a lot of people, especially with disabilities or just more issues who have a really hard time going in person. I feel like there’s a lot more people who were able to access services and I don’t think that they should just be cut off and done. ” Those living on the outskirts of the city or with other commitments had limited time to attend in-person support meetings, making hybrid services desirable after pandemic restrictions were lifted. Online meetings made mental health services more accessible, allowing individuals to manage their work-life domains more harmoniously. A peer said: “… People are always finding it a stress release and I like accessing it (peer support) from home sometimes instead of having to go to places…Sometimes I’m just not into seeing people, or going out and dealing with traffic.”

In sum, continuing with virtual services while also maintaining in-person services was seen as offering more access to peer support services to a broader population, and as providing more choice for individuals who sought peer support.

This study contributes to the literature in a number of ways. It emphasizes the importance of providing virtual peer support in situations where mental health in-person support and services are not possible or accessible. We have highlighted the technology-based challenges and opportunities that create boundaries and bridges respectively to peer support in a virtual space. We have shown that a hybrid model involving both virtual and in-person services offers better accessibility to individuals and groups in need of support, and have argued for the importance of maintaining both modalities. We have also shown that a sense of community can be established in a virtual space, and have highlighted the strategies that peer organizations and their members can utilize to maintain the community spirit. As importantly, we have contributed to the literature by including peer voices and highlighting their experiences in their own words. Researchers have pointed out that the experiences of service users have not been adequately researched [ 26 ] and this is particularly so in the case of peers [ 25 ]. Our research enhances understanding of service users’ lived experiences.

A hybrid model of peer support services

Our findings show, consistent with the literature, that each of virtual and in-person peer support service has its own advantages and disadvantages when used singularly, and that the joint operation of virtual and in-person services through a hybrid model provides more accessible service [ 32 ]. Using both approaches conjointly offers the opportunity to strengthen community-based mental health, and to reinforce recovery approaches that promote individual choice and self-determination. The importance and benefits of peer support and recovery approaches have been documented [ 33 ] and have been implemented increasingly across countries around the globe [ 1 ]. A hybrid model benefits service users in that during health system crises, such as a pandemic caused by an infectious disease when mental health needs are higher, access to mental health support can be maintained. Overall, this model offers promising potential as a vital resource to support the mental well-being of populations.

Using both models conjointly benefits not only service users and communities but also organizations that support mental health. By maintaining and strengthening both types of services, organizations that provide mental health services can build their capacities and be better prepared for sudden changes that might require suspending or limiting in-person services. This enhances flexibility and adaptability by maintaining a system that can dynamically switch between the two modalities.

Yet, despite the benefits of maintaining virtual services alongside in-person services, some PSWs and peers in our study reported a number of technology-related challenges that included difficulties obtaining internet connection or proper equipment, as well as limited skills with respect to the use of technology. Our findings are consistent with research which shows that providers and users of virtual mental health services report several limitations, such as difficulties with the adoption of the remote practice, and access and literacy challenges [ 11 , 34 , 35 , 36 , 37 ]. Our findings also show that to be effective, a mental health support system that utilizes a virtual mode of service delivery requires appropriate technological tools and infrastructure, as well as appropriate support. In the case we studied, the organization advocated for and obtained access to the internet and equipment for peers. Further, the organization allocated extensive time to the training of PSWs and peers. PSWs, once versed on the use of the technology, offered help to peers in group settings and one-on-one when necessary. This kind of assistance and collaboration is common in peer support communities, where principles of mutuality and cooperation prevail, but this also suggests the importance of providing adequate resources to peer support communities so they can achieve their full potential.

Another challenge associated with the virtual environment is that computer-mediated communications provide fewer social context cues; hence individuals who join an online community may experience less personal connection [ 23 ]. This challenge was identified by some of our participants, prompting us to ask how a sense of community may be established and maintained when peers connect virtually.

• Sense of community

Ilioudi et al. (2012) refer to virtual communities in health care as “a group of people using telecommunication with the purposes of delivering health care and education, and/or providing support” [ 38 , p.1]. These communities encompass a wide range of clinical services and technologies. During the COVID-19 pandemic, there was increasing attention to online recovery services and phone support, self-help and mental health self-management delivered virtually or in e-communities [ 39 ]. E-communities are critical for mental health support and have the potential to transform the philosophical approach to the provision of mental health services as they help bridge the gap between the high prevalence of mental health challenges and the relatively low capacity of mental health systems [ 40 ].

In peer support communities, individuals share experiential knowledge to encourage and pursue recovery as a mutual goal, showing common purpose and interdependence [ 41 , 42 ]. Despite many peer support e-communities having been set up and having flourished during the COVID-19 pandemic and thereafter, there has been limited research on how the sense of community can be established or maintained in these groups. In studies of groups and communities more generally (and not only in the case of peer support), there has been focus on applying quantitative measurements and scales for the assessment of the sense of community, e.g., the Brief Sense of Community Index [ 43 ], and the Brief Sense of Community Scale [ 44 ]. These scales have been applied to study academic communities of practice [ 18 ], online education programs for different groups [ 45 – 46 ] and for individuals with serious mental illness living in community settings [ 47 ]. However, less research applies qualitative methods to explore in more depth this sense of community.

Literature shows that a sense of community is important in mental health support, especially during crises such as the COVID-19 pandemic [ 48 ]. A better understanding of the sense of community in virtual services could uncover factors that contribute to a positive therapeutic environment [ 49 ]. Our results identified five strategies to maintain a sense of community amongst peers and providers in a virtual environment during the COVID-19 pandemic. These findings highlight the importance of having a holistic and multidimensional perspective where the organization, providers, and peers all play a role.

The strategies we identified resonate with McMillan and Chavis’ conceptualization of a sense of community [ 20 ]. Their conceptualization highlights four elements: (a) membership (a feeling of belonging), (b) influence (a sense of mattering to the group), (c) integration and fulfillment of needs (a feeling that needs will be met through membership in the group), and (d) shared emotional connection (a belief that members have shared history and similar experiences). By “ acting collectively” (as in our findings), individuals reinforce the notion that they belong to a community where their contributions matter and are valued. Acting collectively also allows the community to fulfill common needs. “ Building on organizational and peer culture ” involves recognizing the contributions of individual members that could reinforce the belief that each member has a meaningful impact on the community. This culture is inclusive and fosters integration and emotional connection among the members. “ Establishing multiple points of connection ” ensures that community members have diverse channels to interact, collaborate, and meet their needs. “ Maintaining a continuous presence and social interaction ” helps establish trust that membership in the community is a reliable path for meeting their needs. Finally, “ sharing lived experiences and learning together ” allows members to open up about their mental health (or other) challenges, contributing to an emerging collective narrative and shared history. Other organizations attempting to build or maintain a sense of community in a virtual space may find some of these strategies employed by the organization, the PSWs and the peers to be helpful.

Limitations and directions for future research

Our study has a number of limitations. Concerns regarding security and privacy in virtual health care communities have been highlighted in research [ 10 , 50 ]. Researchers have also pointed to potential conflicts within online communities set up for various purposes [ 51 , 52 ]. Our paper did not examine these privacy and social concerns, however, evidence regarding these topics is important to provide guidance on how to make virtual spaces safe for peers who participate. Future research on these topics would be useful.

In addition, our findings pointed to peer support users who did not access the mental health support services when these transitioned to virtual platforms. We did not have access to these individuals, and it is not clear what factors contributed to their absence. Future research may explore whether and how technology-based boundaries become an impediment to seeking mental health support for some individuals. We also need a better understanding of the mental health of individuals who stopped using peer support when services moved online.

Our study focused on an organization and its members (PSWs and peers) and did not include in-depth attention to macro system level influences on or implications of peer support in a virtual space. The socio-economic aspects of adopting virtual work and services require further exploration including the financial return on investment and social returns (e.g. recovery) associated with using hybrid mental health support services. Overall, future research may identify and address system level influences that can hinder or facilitate mental health virtual services within community organizations, and how the needs of and services provided by these organizations may influence the allocation of resources and mental health indicators at a systems level.

Implications for policy and practice

Our findings highlight the organization’s efforts to provide accessibility and support for both peers and PSWs and demonstrate the value of a proactive and responsive approach to addressing major change. Organizational and management support has been identified as a central factor in employees’ readiness when change occurs in an organization [ 53 ]. In fact, the COVID-19 pandemic situation highlighted the adaptability and resilience of peer support services and communities. As a manager in our study pointed out, the peer support organization was able to quickly and flexibly respond to the sudden surge in need for mental health support at a time when more institutionalized and strongly professionalized services were struggling to adapt. The resilience and adaptability of peer support organizations and programs are strengths in mental health care systems that are struggling to meet the needs of populations [ 1 ], yet these organizations and programs often receive a relatively small share of health care resources. Future policy may consider a more equitable allocation of resources to peer support services.

Another policy-related implication pertains to technology infrastructure and more specifically to who gets access to devices (such as smart phones and computers) and internet connections. Our study highlighted that lack of access to these resources was a boundary that challenged some peers seeking virtual support services. The peer support organization stepped in to create bridges by advocating with funders and tech providers. However, this leaves unsolved an issue that needs to be addressed at a higher societal level, namely the limited, yet necessary, resources available to some segments of the population (typically homeless individuals, people with disabilities, refugees and other groups). This issue should be an important consideration in future policy.

Finally, our study pointed to several practical implications based on the experience of the case we studied. For example, we pointed to the various strategies that peer organizations can use to maintain a sense of community in a virtual space. Further, in anticipation of the growth of virtual peer support services, organizations may consider the need for renewed training modules that integrate necessary skills relating to using technology for recovery support. Peer support organizations may also consider building their capacity to respond quickly to crises and major changes, as it is during these situations that their services may be in most demand.

The important role of mental health community services and the changing drivers in mental health systems have been noted by researchers. Norton (2023) points out that “ mental health services are currently undergoing immense cultural, philosophical, and organizational change. One such mechanism involved in this change has been the recognition of lived experience as a knowledge subset in its own right ” [ 54 , p.1]. The trends of peer support gaining in importance and being delivered in virtual as well as in-person spaces are poised to continue in the future. It is incumbent on researchers to continue studying the challenges and opportunities of peer support in its various models. Our study has been a step in this direction.

Data availability

The dataset used in this research is not publicly available as set out by the research ethics approval from the University of Ottawa and the consent forms signed by the participants. Further information is available from the corresponding author upon request.

Abbreviations

Peer Support Worker

United Kingdom

United States

World Health Organization

Research Ethics Board

World Health Organization. Peer support mental health services: promoting person-centred and rights-based approaches. In: Guidance and technical packages on community mental health services: Promoting person-centred and rights-based approaches. World Health Organization. 2021. https://www.who.int/publications/i/item/9789240025783

Cyr C, McKee H, O’Hagan M, Priest R. For the Mental Health Commission of Canada. Making the case for peer support: Report to the peer support project committee of the mental health commission of Canada. Mental Health Commission of Canada; 2010/2016. http://www.mentalhealthcommission.ca

Mirbahaeddin E, Chreim S. A narrative review of factors influencing peer support role implementation in mental health systems: implications for research, policy and practice. Adm Policy Ment Health Ment Health Serv Res. 2022;49(4):596–612.

Article   Google Scholar  

Burke E, Pyle M, Machin K, Varese F, Morrison AP. The effects of peer support on empowerment, self-efficacy, and internalized stigma: a narrative synthesis and meta-analysis. Stigma Health. 2019;4(3):337–56.

Ziegler E, Hill J, Lieske B, Klein J, dem OV, Kofahl C. Empowerment in cancer patients: does peer support make a difference? A systematic review. Psycho-oncology. 2022;31(5):683–704.

Article   PubMed   Google Scholar  

Otte I, Werning A, Nossek A, Vollmann J, Juckel G, Gather J. Beneficial effects of peer support in psychiatric hospitals: a critical reflection on the results of a qualitative interview and focus group study. J Ment Health. 2020;29(3):289–95.

Kourgiantakis T, Markoulakis R, Lee E, Hussain A, Lau C, Ashcroft R, Goldstein AL, Kodeeswaran S, Williams CC, Levitt A. Access to mental health and addiction services for youth and their families in Ontario: perspectives of parents, youth, and service providers. Int J Ment Health Syst. 2023;17(1):1–15.

Fisher EB, Miller SM, Evans M, Luu SL, Tang PY, Dreyer Valovcin D, Castellano C. COVID-19, stress, trauma, and peer support—observations from the field. Transl Behav Med. 2020;10(3):503–5.

Article   PubMed   PubMed Central   Google Scholar  

Fortuna KL, Myers AL, Walsh D, Walker R, Mois G, Brooks JM. Strategies to increase peer support specialists’ capacity to use digital technology in the era of COVID-19: pre-post study. JMIR Ment Health. 2020;7(7):e20429.

Moreno C, Wykes T, Galderisi S, Nordentoft M, Crossley N, Jones N, Arango C. How mental health care should change as a consequence of the COVID-19 pandemic. Lancet Psychiatry. 2020;7(9):813–24.

Witteveen AB, Young S, Cuijpers P, Ayuso-Mateos JL, Barbui C, Bertolini F, Cabello M, Cadorin C, Downes N, Franzoi D, Gasior M. Remote mental health care interventions during the COVID-19 pandemic: an umbrella review. Behav Res Ther. 2022 Nov;11:104226.

Di Carlo F, Sociali A, Picutti E, Pettorruso M, Vellante F, Verrastro V, di Giannantonio M. Telepsychiatry and other cutting-edge technologies in COVID-19 pandemic: bridging the distance in mental health assistance. Int J Clin Pract. 2020;75(1).

Makarius EE, Larson BZ. Changing the perspective of virtual work: building virtual intelligence at the individual level. Acad Manag Perspect. 2017;31(2):159–78.

Baard SK, Rench TA, Kozlowski SWJ. Performance adaptation: a theoretical integration and review. J Manag. 2014;40(1):48–99.

Google Scholar  

Reyt JN, Wiesenfeld BM. Seeing the forest for the trees: exploratory learning, mobile technology, and knowledge workers’ role integration behaviors. Acad Manag J. 2015;58(3):739–62.

Suresh R, Alam A, Karkossa Z. Using peer support to strengthen mental health during the COVID-19 pandemic: a review. Front Psychiatry. 2021;12:1119.

Stewart K, Townley G. How far have we come? An integrative review of the current literature on sense of community and well-being. Am J Community Psychol. 2020;66(1–2):166–89.

Nistor N, Daxecker I, Stanciu D, Diekamp O. Sense of community in academic communities of practice: predictors and effects. High Educ. 2015;69:257–73.

Gusfield JR. The community: a critical response. New York: Harper Colophon; 1975.

McMillan DW, Chavis DM. Sense of community: a definition and theory. J Community Psychol. 1986;14(1):6–23.

Mead S, Hilton D, Curtis L. Peer support: a theoretical perspective. Psychiatr Rehabil J. 2001;25(2):134–41.

Article   CAS   PubMed   Google Scholar  

Stanley J, Stanley J, Hensher D. Mobility, social capital and sense of community: what value? Urban Stud. 2012;49(16):3595–609.

Rovai AP. Building sense of community at a distance. Int Rev Res Open Distrib Learn. 2002;3(1):1–16.

Cronenwett LR, Norris AE. Peer support for persons with co-occurring disorders and community tenure: a review of the literature. J Dual Diagn. 2009;5(3–4):279–90.

Walker G, Bryant W. Peer support in adult mental health services: a metasynthesis of qualitative findings. Psychiatr Rehabil J. 2013;36(1):28–34.

Appleton R, Williams J, Vera San Juan N, Needle JJ, Schlief M, Jordan H, Sheridan Rains L, Goulding L, Badhan M, Roxburgh E, Barnett P, Spyridonidis S, Tomaskova M, Mo J, Harju-Seppänen J, Haime Z, Casetta C, Papamichail A, Lloyd-Evans B, Simpson A, Johnson S. Implementation, adoption, and perceptions of telemental health during the COVID-19 pandemic: systematic review. J Med Internet Res. 2021;23(12):e31746.

Yin RK. Case study research and applications. 6th ed. Thousand Oaks, CA: Sage; 2018 Jan.

Miles MB, Huberman AM, Saldaña J. Qualitative data analysis: a methods sourcebook. 4th ed. Thousand Oaks, CA: Sage; 2020.

Marshall CR, Rossman GB, Blanco GL. Designing qualitative research. 7th ed. SAGE; 2022.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Lincoln YS, Guba EG. Naturalistic inquiry. Beverly Hills, CA: Sage; 1985.

Book   Google Scholar  

Strand M, Eng LS, Gammon D. Combining online and offline peer support groups in community mental health care settings: a qualitative study of service users’ experiences. Int J Ment Health Syst. 2020;14(1):1–12.

Piat M, Wainwright M, Rivest MP, Sofouli E, von Kirchenheim T, Albert H, Casey R, Labonté L, O’Rourke JJ, LeBlanc S. The impacts of implementing recovery innovations: a conceptual framework grounded in qualitative research. Int J Ment Health Syst. 2022;16(1):49.

Costa M, Reis G, Pavlo A, Bellamy C, Ponte K, Davidson L. Tele-mental health utilization among people with mental illness to access care during the COVID-19 pandemic. Community Ment Health J. 2021;57(4):720–6. https://doi.org/10.1007/s10597-021-00789-7

Kane H, Gourret Baumgart J, El-Hage W, Deloyer J, Maes C, Lebas MC, Marazziti D, Thome J, Fond-Harmant L, Denis F. Opportunities and challenges for professionals in psychiatry and mental health care using digital technologies during the COVID-19 pandemic: systematic review. JMIR Hum Factors. 2022;9(1):e30359.

Pierce BS, Perrin PB, Tyler CM, McKee GB, Watson JD. The COVID-19 telepsychology revolution: a national study of pandemic-based changes in U.S. mental health care delivery. Am Psychol. 2021;76(1):14–25.

Yue J-L, Yan W, Sun Y-K, Yuan K, Su S-Z, Han Y, Ravindran AV, Kosten T, Everall I, Davey CG, Bullmore E, Kawakami N, Barbui C, Thornicroft G, Lund C, Lin X, Liu L, Shi L, Shi J, Lu L. Mental health services for infectious disease outbreaks including COVID-19: a rapid systematic review. Psychol Med. 2020;50(15):2498–513.

Ilioudi S, Lazakidou AA, Glezakos N, Tsironi M. Health-related virtual communities and social networking services. In: Lazakidou AA, editor. Virtual communities, social networks and collaboration. Volume 15. Springer Science & Business Media; 2012. pp. 1–13.

Fortuna KL, Solomon P, Rivera J. An update of peer support/peer provided services underlying processes, benefits, and critical ingredients. Psychiatr Q. 2022;93(2):571–86.

Van Os J, Guloksuz S, Vijn TW, Hafkenscheid A, Delespaul P. The evidence-based group‐level symptom‐reduction model as the organizing principle for mental health care: time for change? World Psychiatry. 2019;18(1):88–96.

Davidson L, Bellamy C, Guy K, Miller R. Peer support among persons with severe mental illnesses: a review of evidence and experience. World Psychiatry. 2012;11(2):123–8.

Solomon P. Peer support/peer provided services underlying processes, benefits, and critical ingredients. Psychiatr Rehabil J. 2004;27(4):392.

Long DA, Perkins DD. Confirmatory factor analysis of the sense of community index and development of a brief SCI. J Community Psychol. 2003;31(3):279–96.

Peterson NA, Speer PW, McMillan DW. Validation of a brief sense of community scale: confirmation of the principal theory of sense of community. J Community Psychol. 2008;36(1):61–73.

Shea P, Richardson J, Swan K. Building bridges to Advance the Community of Inquiry framework for online learning. Educ Psychol. 2022;57(3):148–61.

Baxter G, Hainey T. Remote learning in the context of COVID-19: reviewing the effectiveness of synchronous online delivery. J Res Innov Teach Learn. 2023;16(1):67–81.

Townley G, Kloos B, Wright PA. Understanding the experience of place: expanding methods to conceptualize and measure community integration of persons with serious mental illness. Health Place. 2009;15(2):520–31.

Bowe M, Wakefield JR, Kellezi B, Stevenson C, McNamara N, Jones BA, Sumich A, Heym N. The mental health benefits of community helping during crisis: coordinated helping, community identification and sense of unity during the COVID-19 pandemic. J Community Appl Soc Psychol. 2022;32(3):521–35.

Kitchen P, Williams A, Chowhan J. Sense of community belonging and health in Canada: a regional analysis. Soc Indic Res. 2012;107:103–26.

Lustgarten SD, Garrison YL, Sinnard MT, Flynn AW. Digital privacy in mental healthcare: current issues and recommendations for technology use. Curr Opin Psychol. 2020;36:25–31.

Ferguson J, Taminiau Y. Conflict and learning in inter-organizational online communities: negotiating knowledge claims. J Knowl Manag. 2014;18(5):886–904.

Hauser F, Hautz J, Hutter K, Füller J, Firestorms. Modeling conflict diffusion and management strategies in online communities. J Strateg Inf Syst. 2017;26(4):285–321.

Austin T, Chreim S, Grudniewicz A. Examining health care providers’ and middle-level managers’ readiness for change: a qualitative study. BMC Health Serv Res. 2020;20:47.

Norton MJ. Peer support working: a question of ontology and epistemology. Int J Ment Health Syst. 2023;17(1):1–4.

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Acknowledgements

The authors would like to thank the peer support organization, the peer support workers and the peers who kindly shared their experiences with us.

This research was financially supported by the Partnership Engage Grants COVID-19 Special Initiative from the Social Sciences and Humanities Research Council (SSHRC), Fund # 1008-2020-1020.

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This article is part of EM’s doctoral thesis. EM and SC contributed to the study conception and design. Data collection was done in collaboration, and analysis and manuscript drafting were performed by EM, and were thoroughly reviewed by SC. Both authors critically revised the drafts until finalized.

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Correspondence to Elmira Mirbahaeddin .

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This research involving human participants was granted ethical approval through the Research Ethics Board (REB) of the University of Ottawa (Reference number S-11-20-6226). We confirm that all methods were carried out in accordance with relevant guidelines and regulations. All participants in the study were given oral and written information about the project, and they provided informed consent. In the consent form, we provided resources for mental health support in case of need. In addition, the participants were informed of the possibility of withdrawal from the study.

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Mirbahaeddin, E., Chreim, S. Transcending technology boundaries and maintaining sense of community in virtual mental health peer support: a qualitative study with service providers and users. BMC Health Serv Res 24 , 510 (2024). https://doi.org/10.1186/s12913-024-10943-y

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Received : 12 January 2024

Accepted : 02 April 2024

Published : 24 April 2024

DOI : https://doi.org/10.1186/s12913-024-10943-y

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