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What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

  • Your overall research objectives and approach
  • Whether you’ll rely on primary research or secondary research
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods
  • The procedures you’ll follow to collect data
  • Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

  • Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

  • How much time do you have to collect data and write up the research?
  • Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
  • Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
  • Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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articles about research design

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

  • Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
  • Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

  • Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

  • How many participants do you need for an adequate sample size?
  • What inclusion and exclusion criteria will you use to identify eligible participants?
  • How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

  • The distribution of the data (e.g., the frequency of each score on a test)
  • The central tendency of the data (e.g., the mean to describe the average score)
  • The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

  • Make estimates about the population based on your sample data.
  • Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

  • Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
  • Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

  • Your research questions and/or hypotheses
  • Your overall approach (e.g., qualitative or quantitative )
  • The type of design you’re using (e.g., a survey , experiment , or case study )
  • Your data collection methods (e.g., questionnaires , observations)
  • Your data collection procedures (e.g., operationalization , timing and data management)
  • Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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  • Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

  • Your overall aims and approach
  • The type of research design you’ll use
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods
  • The procedures you’ll follow to collect data
  • Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

  • Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

  • How much time do you have to collect data and write up the research?
  • Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
  • Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
  • Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

  • How many participants do you need for an adequate sample size?
  • What inclusion and exclusion criteria will you use to identify eligible participants?
  • How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

  • The distribution of the data (e.g., the frequency of each score on a test)
  • The central tendency of the data (e.g., the mean to describe the average score)
  • The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

  • Make estimates about the population based on your sample data.
  • Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
  • If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Research design: qualitative, quantitative, and mixed methods approaches / sixth edition

  • Published: 15 November 2023
  • Volume 58 , pages 1011–1013, ( 2024 )

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  • James P. Takona   ORCID: orcid.org/0009-0001-4591-8136 1  

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This review examines John W. Creswell and David Creswell’s sixth edition, which covers the most popular research methods, offering readers a comprehensive understanding and practical guidance in qualitative, quantitative, and mixed methods. The review includes observations on existing drawbacks, gaps, and ideas on potential areas for improvement in the book. The book is an excellent entry point for understanding the three broad research designs. It stands out for incorporating various methods and empowering researchers to effectively align them with specific research questions, objectives, and philosophical underpinnings. However, it could be further refined by incorporating newer research approaches and expanding practical aspects such as data collection, sampling strategies, and data analysis techniques. With these improvements, the sixth edition could further solidify its position as a comprehensive and accessible guide adeptly catering to researchers, educators, and students. Despite the book’s many strengths, there are opportunities for refinement in future editions, incorporating newer approaches to research designs and expanding practical aspects such as data collection, sampling strategies, and data analysis techniques. This review highlights that, with these suggested improvements, future editions could not only maintain but also enhance the text’s comprehensive and accessible nature, further solidifying its status as a vital resource for researchers, educators, and student.

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Takona, J.P. Research design: qualitative, quantitative, and mixed methods approaches / sixth edition. Qual Quant 58 , 1011–1013 (2024). https://doi.org/10.1007/s11135-023-01798-2

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Research design for qualitative and quantitative studies

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

  • Research design types for quantitative studies
  • Video explainer : quantitative research design
  • Research design types for qualitative studies
  • Video explainer : qualitative research design
  • How to choose a research design
  • Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

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Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

The four most common quantitative research design types are descriptive, correlational, experimental and quasi-experimental.

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

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Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

Case study design often involves investigating an individual to gain an in-depth understanding of their experiences, behaviours or outcomes.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

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Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

  • Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
  • Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
  • Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
  • When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Solly Khan

Thanks this was quite valuable to clarify such an important concept.

hetty

Thanks for this simplified explanations. it is quite very helpful.

Belz

This was really helpful. thanks

Imur

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

Sam Msongole

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

Robyn Pritchard

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

kelebogile

how to cite this page

Peter

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

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What is a Research Design? Definition, Types, Methods and Examples

By Nick Jain

Published on: September 8, 2023

What is Research Design?

Table of Contents

What is a Research Design?

12 types of research design, top 16 research design methods, research design examples.

A research design is defined as the overall plan or structure that guides the process of conducting research. It is a critical component of the research process and serves as a blueprint for how a study will be carried out, including the methods and techniques that will be used to collect and analyze data. A well-designed research study is essential for ensuring that the research objectives are met and that the results are valid and reliable.

Key elements of research design include:

  • Research Objectives: Clearly define the goals and objectives of the research study. What is the research trying to achieve or investigate?
  • Research Questions or Hypotheses: Formulating specific research questions or hypotheses that address the objectives of the study. These questions guide the research process.
  • Data Collection Methods: Determining how data will be collected, whether through surveys, experiments, observations, interviews, archival research, or a combination of these methods.
  • Sampling: Deciding on the target population and selecting a sample that represents that population. Sampling methods can vary, such as random sampling, stratified sampling, or convenience sampling.
  • Data Collection Instruments: Developing or selecting the tools and instruments needed to collect data, such as questionnaires, surveys, or experimental equipment.
  • Data Analysis: Defining the statistical or analytical techniques that will be used to analyze the collected data. This may involve qualitative or quantitative methods , depending on the research goals.
  • Time Frame: Establishing a timeline for the research project, including when data will be collected, analyzed, and reported.
  • Ethical Considerations: Addressing ethical issues, including obtaining informed consent from participants, ensuring the privacy and confidentiality of data, and adhering to ethical guidelines.
  • Resources: Identifying the resources needed for the research , including funding, personnel, equipment, and access to data sources.
  • Data Presentation and Reporting: Planning how the research findings will be presented and reported, whether through written reports, presentations, or other formats.

There are various research designs, such as experimental, observational, survey, case study, and longitudinal designs, each suited to different research questions and objectives. The choice of research design depends on the nature of the research and the goals of the study.

A well-constructed research design is crucial because it helps ensure the validity, reliability, and generalizability of research findings, allowing researchers to draw meaningful conclusions and contribute to the body of knowledge in their field.

Types of Research Design

Understanding the nuances of research design is pivotal in steering your investigation towards success. Delving into various research designs empowers researchers to craft tailored methodologies to address specific queries and attain precise objectives. Here, we unveil a spectrum of research designs, meticulously curated to cater to diverse research pursuits.

1. Experimental Research Design

Randomized Controlled Trial (RCT): Immerse yourself in the realm of experimentation with RCTs. Randomly assigning individuals to either an experimental or control group enables meticulous assessment of interventions or treatments’ efficacy.

2. Quasi-Experimental Research Design

Non-equivalent Group Design: When randomness isn’t viable, non-equivalent group designs offer a pragmatic alternative. Comparison across multiple groups without random assignment ensures ethical and feasible research conduct.

3. Observational Research Design

Cross-Sectional Study: Capture snapshots of data at a single moment with cross-sectional studies, unraveling intricate relationships and disparities between variables.

Longitudinal Study: Embark on a journey through time with longitudinal studies, tracking participants’ trajectories to discern evolving trends and patterns.

4. Descriptive Research Design

Survey Research: Dive into the depths of data collection through surveys, extracting insights into attitudes, characteristics, and opinions.

Case Study: Engage in profound exploration through case studies, dissecting singular individuals, groups, or phenomena to unravel profound insights.

5. Correlational Research Design

Correlational Study: Traverse the realm of correlations, scrutinizing interrelationships between variables while refraining from inferring causality.

6. Ex Post Facto Research Design

Retrospective Exploration: Explore existing conditions and behaviors retrospectively, shedding light on potential causes where variable manipulation isn’t feasible.

7. Exploratory Research Design

Pilot Study: Initiate your research odyssey with pilot studies, laying the groundwork for comprehensive investigations while refining research procedures.

8. Cohort Study

Chronicle of Evolution: Embark on longitudinal expeditions with cohort studies, monitoring cohorts to elucidate the evolution of specific outcomes over time.

9. Action Research

Driving Change: Collaboratively navigate practical challenges with action research, fostering improvements in educational or organizational settings.

10. Meta-Analysis

Synthesizing Insights: Merge insights from multiple studies with meta-analyses, presenting a holistic overview of research findings.

11. Cross-Sequential Design

Bridging the Gap: Seamlessly blend cross-sectional and longitudinal elements to dissect age-related changes across diverse cohorts.

12. Grounded Theory

Rooted Insights: Plunge into the depths of qualitative research with grounded theory, crafting theories grounded in meticulously collected data.

Selecting the optimal research design is akin to sculpting a masterpiece, contingent on the intricacies of the research query, resource availability, ethical considerations, and the desired data intricacies. Researchers adeptly navigate these choices to seamlessly align their methodologies with their research ambitions, ensuring both precision and impact.

Learn more: What is Research?

Research design methods refer to the systematic approaches and techniques used to plan, structure, and conduct a research study. The choice of research design method depends on the research questions, objectives, and the nature of the study. Here are some key research design methods commonly used in various fields:

1. Experimental Method

Controlled Experiments: In controlled experiments, researchers manipulate one or more independent variables and measure their effects on dependent variables while controlling for confounding factors.

2. Observational Method

Naturalistic Observation: Researchers observe and record behavior in its natural setting without intervening. This method is often used in psychology and anthropology.

Structured Observation: Observations are made using a predetermined set of criteria or a structured observation schedule.

3. Survey Method

Questionnaires: Researchers collect data by administering structured questionnaires to participants. This method is widely used for collecting quantitative research data.

Interviews: In interviews, researchers ask questions directly to participants, allowing for more in-depth responses. Interviews can take on structured, semi-structured, or unstructured formats.

4. Case Study Method

Single-Case Study: Focuses on a single individual or entity, providing an in-depth analysis of that case.

Multiple-Case Study: Involves the examination of multiple cases to identify patterns, commonalities, or differences.

5. Content Analysis

Researchers analyze textual, visual, or audio data to identify patterns, themes, and trends. This method is commonly used in media studies and social sciences.

6. Historical Research

Researchers examine historical documents, records, and artifacts to understand past events, trends, and contexts.

7. Action Research

Researchers work collaboratively with practitioners to address practical problems or implement interventions in real-world settings.

8. Ethnographic Research

Researchers immerse themselves in a particular cultural or social group to gain a deep understanding of their behaviors, beliefs, and practices.

9. Cross-sectional and Longitudinal Surveys

Cross-sectional surveys collect data from a sample of participants at a single point in time.

Longitudinal surveys collect data from the same participants over an extended period, allowing for the study of changes over time.

Researchers conduct a quantitative synthesis of data from multiple studies to provide a comprehensive overview of research findings on a particular topic.

11. Mixed-Methods Research

Combines qualitative and quantitative research methods to provide a more holistic understanding of a research problem.

A qualitative research method that aims to develop theories or explanations grounded in the data collected during the research process.

13. Simulation and Modeling

Researchers use mathematical or computational models to simulate real-world phenomena and explore various scenarios.

14. Survey Experiments

Combines elements of surveys and experiments, allowing researchers to manipulate variables within a survey context.

15. Case-Control Studies and Cohort Studies

These epidemiological research methods are used to study the causes and risk factors associated with diseases and health outcomes.

16. Cross-Sequential Design

Combines elements of cross-sectional and longitudinal research to examine both age-related changes and cohort differences.

The selection of a specific research design method should align with the research objectives, the type of data needed, available resources, ethical considerations, and the overall research approach. Researchers often choose methods that best suit the nature of their study and research questions to ensure that they collect relevant and valid data.

Learn more: What is Research Objective?

Research Design Examples

Research designs can vary significantly depending on the research questions and objectives. Here are some examples of research designs across different disciplines:

  • Experimental Design: A pharmaceutical company conducts a randomized controlled trial (RCT) to test the efficacy of a new drug. Participants are randomly assigned to two groups: one receiving the new drug and the other a placebo. The company measures the health outcomes of both groups over a specific period.
  • Observational Design: An ecologist observes the behavior of a particular bird species in its natural habitat to understand its feeding patterns, mating rituals, and migration habits.
  • Survey Design: A market research firm conducts a survey to gather data on consumer preferences for a new product. They distribute a questionnaire to a representative sample of the target population and analyze the responses.
  • Case Study Design: A psychologist conducts a case study on an individual with a rare psychological disorder to gain insights into the causes, symptoms, and potential treatments of the condition.
  • Content Analysis: Researchers analyze a large dataset of social media posts to identify trends in public opinion and sentiment during a political election campaign.
  • Historical Research: A historian examines primary sources such as letters, diaries, and official documents to reconstruct the events and circumstances leading up to a significant historical event.
  • Action Research: A school teacher collaborates with colleagues to implement a new teaching method in their classrooms and assess its impact on student learning outcomes through continuous reflection and adjustment.
  • Ethnographic Research: An anthropologist lives with and observes an indigenous community for an extended period to understand their culture, social structures, and daily lives.
  • Cross-Sectional Survey: A public health agency conducts a cross-sectional survey to assess the prevalence of smoking among different age groups in a specific region during a particular year.
  • Longitudinal Study: A developmental psychologist follows a group of children from infancy through adolescence to study their cognitive, emotional, and social development over time.
  • Meta-Analysis: Researchers aggregate and analyze the results of multiple studies on the effectiveness of a specific type of therapy to provide a comprehensive overview of its outcomes.
  • Mixed-Methods Research: A sociologist combines surveys and in-depth interviews to study the impact of a community development program on residents’ quality of life.
  • Grounded Theory: A sociologist conducts interviews with homeless individuals to develop a theory explaining the factors that contribute to homelessness and the strategies they use to cope.
  • Simulation and Modeling: Climate scientists use computer models to simulate the effects of various greenhouse gas emission scenarios on global temperatures and sea levels.
  • Case-Control Study: Epidemiologists investigate a disease outbreak by comparing a group of individuals who contracted the disease (cases) with a group of individuals who did not (controls) to identify potential risk factors.

These examples demonstrate the diversity of research designs used in different fields to address a wide range of research questions and objectives. Researchers select the most appropriate design based on the specific context and goals of their study.

Learn more: What is Competitive Research?

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  • Published: 07 July 2022

The use of co-production, co-design and co-creation to mobilise knowledge in the management of health conditions: a systematic review

  • Cheryl Grindell 1 ,
  • Elizabeth Coates 2 ,
  • Liz Croot 1 &
  • Alicia O’Cathain 1  

BMC Health Services Research volume  22 , Article number:  877 ( 2022 ) Cite this article

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Knowledge mobilisation is a term used in healthcare research to describe the process of generating, sharing and using evidence. ‘Co’approaches, such as co-production, co-design and co-creation, have been proposed as a way of overcoming the knowledge to practice gap. There is a need to understand why researchers choose to adopt these approaches, how they achieve knowledge mobilisation in the management of health conditions, and the extent to which knowledge mobilisation is accomplished.

Studies that explicitly used the terms co-production, co-design or co-creation to mobilise knowledge in the management of health conditions were included. Web of Science, EMBASE via OvidSP, MEDLINE via OvidSP and CINHAL via EBSCO databases were searched up to April 2021. Quality assessment was carried out using the Joanna Briggs Institute qualitative quality assessment checklist. Pluye and Hong’s seven steps for mixed studies reviews were followed. Data were synthesised using thematic synthesis.

Twenty four international studies were included. These were qualitative studies, case studies and study protocols. Key aspects of ‘co’approaches were bringing people together as active and equal partners, valuing all types of knowledge, using creative approaches to understand and solve problems, and using iterative prototyping techniques. Authors articulated mechanisms of action that included developing a shared understanding, identifying and meeting needs, giving everyone a voice and sense of ownership, and creating trust and confidence. They believed these mechanisms could produce interventions that were relevant and acceptable to stakeholders, more useable and more likely to be implemented in healthcare. Varied activities were used to promote these mechanisms such as interviews and creative workshops. There appeared to be a lack of robust evaluation of the interventions produced so little evidence in this review that ‘co’approaches improved the management of health conditions.

Those using ‘co’approaches believed that they could achieve knowledge mobilisation through a number of mechanisms, but there was no evidence that these led to improved health. The framework of key aspects and mechanisms of ‘co’approaches developed here may help researchers to meet the principles of these approaches. There is a need for robust evaluation to identify whether ‘co’approaches produce improved health outcomes.

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PROSPERO CRD42020187463 .

Peer Review reports

The term ‘knowledge mobilisation’ is used in the healthcare literature to describe the active, iterative and collaborative process of creating, sharing and using research evidence [ 1 , 2 ]. Ideally all forms of knowledge, such as experience, values and beliefs are considered in this process—not just scientific factual knowledge [ 3 , 4 ]. This is in contrast to the term ‘evidence’ where patients’ voices are considered bottom of the evidence hierarchy [ 4 ]. Research and healthcare practice inhabit very different worlds, with contrasting goals and using different languages [ 4 ]. A shift from hierarchical models of evidence, that favour scientific/medical knowledge, to other forms where patient voice is more at the forefront has been recommended [ 4 ]. This has led to a change from linear, rational approaches to knowledge mobilisation to more disordered, relational, context driven ones [ 4 , 5 ]. Knowledge mobilisation as a concept remains confusing and is often considered an umbrella term for other forms of knowledge sharing and use such as knowledge translation, exchange and dissemination [ 3 , 5 , 6 ]. These terms are frequently used interchangeably within the literature.

Involving patients and clinicians in the generation of new knowledge is considered important to ensure research findings are impactful and to reduce research waste [ 7 , 8 ]. The need to make public services evidence-based remains of high importance [ 5 ] in order to improve the management of health conditions such as cardiovascular disease, osteoarthritis and cancer. Many of these health conditions require long term management that place high burden on healthcare services [ 9 ]. Sharing and generating knowledge between patients and clinicians can help improve understanding of living with and treating these conditions. This can positively impact disease progression, burden of care and health outcomes [ 9 ]. However involving patients and clinicians in research or service improvement is challenging and sometimes tokenistic [ 7 ]. Social hierarchies exist which means not all knowledge is valued and considered equally [ 10 ]. Co-creative approaches to knowledge production have been advocated to bridge the knowledge-to-practice gap [ 5 , 8 ]. There are many different collaborative and participatory methods in the health research and service improvement literature [ 7 ], with a multitude of approaches being used. Co-production, co-design and co-creation are common terms; these terms have been summarised as ‘co’approaches [ 11 ]. The fundamentals of ‘co’approaches have been described in the literature, for example the UK’s National Institute for Health Research (NIHR) principles for co-production [ 12 ]. Despite this, there is little consensus about the type of approaches the three terms describe [ 11 , 13 ]. Common uses of these terms are: 1) co-production of a research project where researchers, practitioners and the public work together throughout the course of the project [ 12 ]; 2) co-creation of new knowledge by academics working alongside other stakeholders [ 8 ] and; 3) co-design when developing complex interventions [ 14 ]. In practice, the three terms are often used interchangeably and adopted and described inadequately and ambiguously [ 11 , 15 ]. Many ‘co’approaches do not address the egalitarian and utilitarian values of what is considered ‘genuine’ co-production leading to a crowded landscape of terms and approaches beginning with the word ‘co’ that Williams et al. (2019) have described as ‘cobiquities’ [ 13 ].

There is currently a lot of interest in knowledge mobilisation and ‘co’approaches in health, with multiple publications about their use. Several reviews have explored the use of specific co-production, co-design or co-creation processes. A recent review undertook content analysis of the co-creation of knowledge for health interventions aiming to reduce the term’s ambiguity and provide a clear definition [ 15 ]. The authors developed a new evidence-based definition of knowledge co-creation but included a number of other ‘co’ terms within this, still leaving the reader to address a confusing landscape of ‘cobiquities’. A rapid review of research co-design in health settings had a specific focus on the planning stages of a research project only [ 16 ]. Another review sought to understand the outcomes associated with developing and implementing co-produced interventions in acute healthcare settings [ 17 ]. The latter reported findings related to understanding the processes of co-designing a service rather than evaluating outcomes themselves. They found different forms of co-production were reported, often uncritically, with a lack of consistent use of terminology to support this diverse range of participatory approaches [ 16 , 17 ].

To the authors’ knowledge there has yet to be a systematic review that has specifically explored the use of ‘co’approaches in knowledge mobilisation in the management of health conditions. This systematic review aimed to explore why researchers use ‘co’approaches, how researchers think ‘co’approaches can achieve health improvement, the activities they use, and whether they achieve knowledge mobilisation in the management of health conditions (actual or perceived).

This is a mixed studies systematic review, that is, a comprehensive review and synthesis of a wide range of literature of diverse designs [ 18 ]. Mixed studies reviews are useful for understanding complex phenomena such as ‘co’approaches for knowledge mobilisation. Seven standard systematic review steps for mixed studies reviews have been followed [ 18 ]: 1. Writing a review question. 2. Defining eligibility criteria. 3. Applying an extensive search strategy in multiple information sources. 4. Identifying potentially relevant studies (by two independent researchers screening titles and abstracts). 5. Selecting relevant studies (based on full text). 6. Appraising the quality of included studies using an appropriate tool. 7. Synthesising included studies.

Conduct and reporting of the review followed the Preferred Reporting Items for Systematic reviews and Meta Analysis checklist and flow chart to ensure transparency and complete reporting of the findings [ 19 ]. The review was registered with PROSPERO (registration number CRD42020187463 September 2020).

Review questions

What is the rationale for using ‘co’approaches to mobilise knowledge in the management of health conditions?

What mechanisms of ‘co’approaches achieve knowledge mobilisation (actual or perceived) in the management of health conditions?

What type of activities are used within ‘co’approaches to mobilise knowledge in the management of health conditions?

To what extent do ‘co’approaches achieve knowledge mobilisation (actual or perceived) to help manage health conditions?

Defining eligibility criteria

Specific inclusion and exclusion criteria were defined using the PICOS framework, Population, Intervention, Context, Outcome and Study type [ 20 ]. See Table 1 . One of three common terms, that is co-production, co-design and co-creation, had to be explicitly used in a paper for inclusion in this review.

Applying an extensive search strategy in multiple information sources

Systematic search of academic literature.

Searches were conducted of four electronic databases: Web of Science (all databases) 1970—April 2021, EMBASE via OvidSP 1988 – April 2021, MEDLINE via OvidSP 1946 – April 2021, CINHAL via EBSCO 1981—April 2021. Initial full database searches were carried out up to 26 th May 2020. Search alerts were used from this point on for all four databases up until the end of April 2021. The University of York’s Centre for Reviews and Dissemination database, the Cochrane Library (CENTRAL) and Trip medical database were also searched. Bibliographic searches of selected articles reference lists were browsed for any additional relevant studies [ 21 ].

Structured search of the grey literature

Grey literature (unpublished) searches were also conducted to identify any literature from non-traditional sources and to minimise publication bias [ 21 ]. Grey literature sources such as Open Grey and Google were conducted as well as websites of professional networks in the field, for example the Canadian Integrated Knowledge Translation (IKT) Network. It is acknowledged that a google search may produce many pages of potentially relevant literature. In this case the first eight pages of the google search were screened. At which point the number of relevant literature significantly diminished. Publications situated on the university profile pages of academic experts in co-production and or knowledge mobilisation were also searched. These were identified through a UK Knowledge Mobilisation Alliance and through recommendations of academic peers. Citation searching from the reference lists of included studies was also carried out.

Search terms

A comprehensive search strategy was developed in conjunction with an information specialist and was performed by the primary reviewer (CG). A wide variety of key search terms, based on terms in the review question, were used. They included free text and subject headings (such as MeSH) where appropriate. Truncationfor certain key words was used for completeness. Boolean logic operators AND / OR were then utilised to combine terms [ 21 ]. For example:

Co-production OR co-prod* OR coproduction OR coproduc* OR co production OR co produc*OR codesign OR co-design OR co design OR co-creat* OR cocreat* OR co creat*

Knowledge mobil* OR Knowledge transl*OR knowledge utili*OR knowledge exchange OR knowledge uptake OR Knowledge to action OR Knowledge to practice OR Evidence based practice.

Search terms were purposely limited to try and provide some focus on what is a very crowded and complex landscape. Multiple terms are often used in the literature for co-productive activities which can be confusing. This systematic review purposely sought to provide some clarity on the use of the three common ‘co’ terms, co-production, co-design and co-creation rather than, for example patient and public involvement and engagement. The same can be said for knowledge mobilisation. Therefore this study limited the use of knowledge mobilisation terms to those frequently seen in the healthcare literature and which encompassed a more interactional, two way flow of knowledge. Implementation was specifically not used, even though it could be argued it is the final stage of knowledge mobilisation, so not to cause confusion between these two different but similar terms and their meanings.

See supplementary material 1 (word document) for detailed search terms used.

Identifying relevant studies

All database search results were imported and organised in Endnote X8 and exported to an Excel spreadsheet. Duplicate references were removed. This selection process allowed for transparency and reproducibility [ 21 ]. Documents were screened by title and then by abstract using the pre-determined eligibility criteria. Any articles that appeared to fulfil the inclusion criteria were obtained in full [ 20 , 22 , 23 ]. One reviewer (CG) screened all citations by title and abstract and a second reviewer (EC) independently screened 50. A high level of agreement was achieved between CG and EC on initial screening (90%). The remaining 10% were uncertainties mainly on CG’s part, who was an early career researcher. These uncertainties were resolved through discussion with EC, a more experienced researcher. It was therefore agreed, due to the high level of initial agreement and lessons learnt through the discussions, that the process was robust enough for CG to review the remaining titles and abstracts.CG then assessed the full text of all potentially eligible studies and EC reviewed 20% of the full text articles. EC provided a second opinion for papers CG was unclear about. CG and EC discussed any uncertainties and disagreements and reached a consensus on which studies to include.

Data extraction and management

A standardised data extraction form was developed and tested on a small number of selected studies and then refined [ 20 , 23 ]. The type of data extracted included: study characteristics such as type of study, setting, participant characteristics, rationale given by researcher for using a ‘co’approach, proposed mechanisms of ‘co’approach, type of activities used and outcomes of ‘co’approach (measured or perceived impact on knowledge mobilisation). The first reviewer (CG) extracted the data from all the included studies and a second reviewer (EC) double extracted 20% of papers to ensure consistency.

Appraising the quality of included studies

There was a mixture of study types in this review including qualitative studies, co-design case studies and study protocols. Five of the 24 papers were mixed methods with qualitative research dominance, that is, they collected survey data alongside the main qualitative findings. The Joanna Briggs Institute (JBI) quality assessment checklists were chosen as they cover a variety of study designs [ 23 ]. Due to the nature of the included studies, the JBI qualitative quality assessment check list was used for all studies as a ‘best fit’. This was because there are no specific checklistsfor study protocols and case studies. Studies were not excluded based on quality as long as they addressed the focus of the review. This was to ensure no rich and meaningful insights from the data were lost [ 24 ]. CG appraised all selected studies and EC double appraised 20% of the selected studies. Any disagreements were resolved through discussion.

S ynthesising included studies

A thematic synthesis approach was used based on the principles of Thomas and Harden (2008) [ 25 ]. This has three stages: line by line coding of text, development of descriptive themes, and generation of analytical themes [ 25 ]. Analytical themes were not relevant for all the research questions so descriptive themes are presented. NVivo QSR (2020) was used to store and organise the extracted data. There was a small amount of quantitative data extracted in this review in the form of descriptive statistics. A convergent integrated approach was used [ 23 , 26 ]. The quantitative data was ‘qualitized’ and turned into textual descriptions and then combined with the qualitative data [ 23 , 26 ]. This allowed for a narrative interpretation of the quantitative results [ 23 ].

Characteristics of studies

The searches identified 1171 studies. After deduplication 782 were screened by title and abstract. This was a challenging task due to the broad and varied use of the terms co-production, co-design, co-creation and knowledge mobilisation in the literature. The remaining 286 articles were reviewed in full text to assess their eligibility, resulting in 24 included in the review. See Fig.  1 .

figure 1

PRISMA 2020 flow diagram [ 19 ]

Study characteristics are shown in Table 2 . The included studies were conducted internationally: in the UK ( n  = 9) [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], Australia ( n  = 7) [ 36 , 37 , 38 , 39 , 40 , 41 , 42 ], Canada ( n  = 5) [ 43 , 44 , 45 , 46 , 47 ], Sweden ( n  = 2) [ 48 , 49 ] and Italy/UK ( n  = 1) [ 50 ]. The majority of the studies were qualitative case studies [ 27 , 28 , 29 , 32 , 33 , 35 , 36 , 38 , 39 , 40 , 41 , 50 ]. Five of these studies collected and presented survey data alongside the narrative data [ 30 , 42 , 43 , 48 , 49 ]. Three papers were qualitative study protocols [ 31 , 37 , 47 ]. One was a patient-led (co-designed) qualitative study [ 46 ], and there were three case study collections [ 34 , 44 , 45 ]. Numbers of participants varied across studies from 7- 156. All three terms co-production [ 28 , 29 , 32 , 33 , 34 , 35 , 50 ], co-design [ 28 , 30 , 31 , 34 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 48 , 49 ] and co-creation [ 36 , 45 , 47 ], were used to define their knowledge mobilisation approach.

Quality of studies

Eighteen out of the 24 papers were assessed as moderate to high quality. Three papers—two non-peer reviewed casebooks and a study protocol, were assessed as low quality. Another three papers were deemed low-moderate quality and consisted of another casebook, a study protocol and a qualitative case study. The latter was assessed as low quality due to unclear reporting. It is possible that the casebooks and study protocols scored poorly due to the lack of appropriate assessment tools for these types of publications. (see Table 3 ).

Overview of Themes

Overall four themes were identified: 1. Key aspects of ‘co’approaches for knowledge mobilisation. 2. Mechanisms of action. 3. Activities used. 4. Outcomes of ‘co’approaches for knowledge mobilisation. The themes and their sub-themes, along with the relationships between them, are illustrated in Fig.  2 .

figure 2

Overview of themes: key aspects, mechanisms of action, activities used and outcomes of ‘co’approaches for knowledge mobilisation in health conditions

Key aspects of ‘co’ approaches for knowledge mobilisation

The aspects of ‘co’approaches that authors proposed as important to mobilise knowledge to improve the management of health conditions included: bringing people together as active and equal partners, valuing all knowledge, using a creative approach, and iterative prototyping techniques.

Bringing diverse people together as active and equal partners

Forming collaborations between different stakeholders was considered critical [ 29 , 32 , 36 , 38 , 42 , 47 ]. Authors believed that partnership working led to the sharing of goals [ 35 ], responsibilities and decision making throughout the process [ 27 , 30 , 31 , 44 , 47 , 48 ]. Involving the right people in the ‘co’approach was considered to be central to knowledge mobilisation. For example, one study recognized that :

‘involving all stakeholders can provide richer insights than involving patients or professionals alone’ [ 30 ].

Another proposed that by promoting inclusivity:

‘ meaningful egalitarian partnerships are formed between participants ’ [ 28 ].

Actively engaging stakeholders was identified as important [ 28 , 31 , 35 , 36 , 38 ], where they are:

‘ active agents not merely passive subjects or recipients of services’ [ 29 ].

Valuing all knowledge

Authors acknowledged the existence of disparate types of knowledge in terms of research evidence, experience and opinions. They highlighted the need to include, recognise and understand all knowledge [ 27 , 31 , 32 , 41 , 44 , 49 ] and place equal importance [ 29 ] on evidence-based research knowledge, clinical knowledge and experiential knowledge [ 27 , 28 , 36 , 40 , 47 , 50 ]. Some authors suggested that ‘co’approaches offered an opportunity to generate, share and gain locally generated knowledge and experience from different sources [ 28 , 30 , 36 , 48 ].

‘ Our approach is potentially efficient in making use of all available knowledge (scientific and ‘practical’); and potentially effective in being grounded in the reality and complexity of applied practice’ [ 33 ].

Using a creative approach

Collaborative ways of working, inherent in ‘co’approaches, were deemed to be significantly different to the usual way of doing applied health research [ 29 , 39 ]:

‘the researchers and clinicians in some of the projects found that their experience of working in collaboration on the projects was different to how they had carried out research before (‘game changers’) and opened up new possibilities and capacity’ [ 29 ] .

Design and creative practice were recognised as a means to successfully bring the knowledge, skills, expectations and beliefs of heterogeneous groups of people together [ 28 , 32 , 34 , 50 ]. Encouraging those involved to think and behave in different ways [ 29 , 30 ] enhancing idea generation [ 39 , 41 ].

Maintaining engagement of stakeholders was recognised as difficult. One study found that despite regular project meetings and media awareness campaigns they did not maintain engagement of key stakeholders through to implementation [ 36 ]. In contrast other studies [ 38 , 41 , 44 ] that favoured creative activities, felt that their design and participatory methods helped to engage diverse groups of people with varying goals, feelings and abilities. They perceived that their ‘co’approach helped retain engagement even within those groups who do not traditionally get involved in research [ 34 , 35 , 39 , 50 ]:

‘ The research and development cycle that we employed in this study is an optimal methodology to engage, retain, and work more efficiently with hard-to-reach populations’ [ 39 ].

Innovative, iterative and prototyping techniques

Many of the study authors proposed to use a flexible, iterative process to achieve successful knowledge mobilisation [ 27 , 28 , 30 , 33 , 35 , 44 , 46 ]. For example, the iterative PaCER process in one study allowed learning from participants in each phase to inform the next [ 46 ]. Another felt that flexibility was essential to adapt knowledge to context in a complex dynamic system such as healthcare [ 33 ].

Iterative prototyping, often used in design practice, was adopted in a number of studies [ 28 , 30 , 31 , 34 , 35 , 39 , 40 , 41 ]. Prototyping was considered useful for turning knowledge into practical, tangible objects [ 28 , 34 , 35 ]. For example, one study used quick, easy and cheap, low fidelity prototypes to generate iterative cycles of feedback and development [ 28 ]. In other studies, visual design artefacts such as videos, drawings and sketches were used [ 28 , 31 , 34 , 39 , 40 , 41 , 50 ]. Authors felt that ideas could be quickly communicated in this way in simple, understandable forms making knowledge more accessible [ 28 , 30 , 34 , 50 ].

Expert facilitation of these varied activities was considered to be crucial to their success. The use of independent facilitators was found to be successful [ 34 , 35 , 43 ]. They appeared to reduce anxieties regarding participation and encourage open and honest contributions [ 34 , 43 ]:

‘Having a design facilitator enabled visualisation of thoughts and ideas as they arose. This allowed real time synthesis of occurring knowledge, for example through drawings, which was presented in a form that was easy to understand and which accurately represented participant’s views’ [ 28 ] .

Alternatively training could be given to enable researchers to facilitate these activities successfully [ 30 ].

Mechanisms of action

‘Co’approaches were considered to achieve knowledge mobilisation through a number of mechanisms of action directly related to the key aspects described. Study authors considered that bringing people together as active partners, valuing all forms of knowledge, using a creative approach and iterative prototyping techniques, could facilitate a shared understanding of the problem and identify important needs and how to meet them, thereby balancing power differentials, offering a sense of ownership, and engendering trust and confidence in solutions.

Shared understanding

Authors reported engaging multiple stakeholders in the process could identify wider perspectives and contexts and contribute to a shared understanding of the problems and potential solutions [ 27 , 28 , 30 , 32 , 33 , 38 , 40 , 43 , 46 ].

Using design artefacts to communicate participants’ thoughts and feelings could facilitate the generation of knowledge and develop a mutual understanding of what was important to stakeholders [ 28 , 30 , 34 , 50 ]. The use of personas [ 28 , 30 , 34 , 35 ] and scenarios [ 30 , 34 ] were thought to help distance participants from their own positions and prevent a ‘them and us’ dynamic developing [ 30 ].

‘The persona seemed to be particularly powerful for the professional group and prompted a focus on considering the “whole person” experience that the attendees said they may not have considered otherwise’ [ 30 ] .

This meant that outputs were a consensus between participants, considering all perspectives, rather than the product of situated assumptions, such as what health care professionals think patients want or need [ 30 ].

Identify and meet needs

Authors described that by bringing diverse groups of people together, pooling their ‘creative assets’ [ 29 ], and considering and valuing their different types of knowledge, expertise and perspectives, they could produce outputs that were tailored to everyone’s needs [ 29 , 32 , 38 , 41 , 42 , 46 , 47 , 50 ]. They felt that by including people with lived experience of a health condition in the process they were able to contribute their unique perspectives and ideas [ 29 , 32 , 35 , 48 ] and the research addressed the areas that patients felt were most important [ 36 , 43 ]. This challenged the traditional medical model which assumes the clinician knows best [ 27 , 43 ].

‘because clinical guidelines are often developed using the medical model where clinicians are considered to possess knowledge and expertise over what is best for the patient’ [ 43 ] .

By valuing diverse evidence and knowledge, authors perceived that complex systems and services, such as those in healthcare, could be better understood as no one individual could understand them completely [ 33 , 35 ]. In this way ‘co’approach outputs could attend and align to context [ 28 , 29 , 34 , 38 ] including wider organisational factors [ 29 ]. Authors felt that using creative and iterative prototyping techniques allowed them to challenge and refine ideas into practical concepts that were fit for purpose and more likely to meet stakeholder needs [ 30 , 38 ].

Balancing power and voice

Authors felt that balancing power and voice of those involved aided knowledge mobilisation. Authors felt this was achieved in various ways. Two studies suggested that giving clinicians, patients and the public a more active role in the whole research process meant that they felt valued and had a more equal role [ 29 , 45 ]. In other studies, involving people with lived experience meant their voices were listened to and valued [ 45 , 46 ]. One study used research based theatre to achieve this [ 32 ]:

‘Theatre makers on the panel were able to explain the process of developing research based Theatre and by doing so revealed how the voices of research participants were respected and heard’ [ 32 ] .

A number of studies found that their ‘co’approaches challenged traditional relationships between patients and doctors [ 28 , 34 , 35 , 41 , 50 ] or blurred practice and academic boundaries [ 28 , 33 , 34 , 35 ]:

‘The discussion was not led by power players such as scientists or surgeons that could have used their status to lead the discussion’ [ 50 ] .

Several studies [ 28 , 34 , 35 , 39 , 41 , 50 ] found that the use of creative activities had a positive influence on group dynamics. For example one study felt that their design-led activities enabled participants to:

‘ share and express themselves in an inclusive environment using a common language. ’ [ 28 ].

Another author felt that power hierarchies could be flattened and more voices heard by making ideas tangible [ 34 ]. Creative activities were found to be helpful in engaging people ‘ who might otherwise have struggled to participate’ [ 34 ] and contribute to the process, such as people with verbal communication problems or lower literacy levels [ 34 , 35 ]. Skilled facilitation was recognised as important in order to manage the power asymmetries found in heterogenous groups of people [ 48 ].

Sense of ownership

Authors anticipated that knowledge could be shared and generated by bringing people together to form collaborative partnerships, creating a sense of ownership and common purpose [ 28 , 44 ] that would help reduce the research to practice gap [ 36 ]. Ownership was reinforced by considering context, implementation and by valuing all stakeholder knowledge [ 28 , 29 , 34 ]:

‘These include developing strong cross-sector partnerships with stakeholders to co- create and share emerging knowledge, integrating and utilizing all stakeholders’ relevant expertise and experience and promoting a sense of ownership and common purpose’ [ 44 ] .

Trust and confidence

Authors identified that stakeholders would have more trust and confidence in the final outputs because their needs were identified, a shared understanding was gained, power and voice was attended to and a sense of ownership was achieved [ 28 , 46 ]. A number of authors deemed their outputs to be more credible, relevant, practical, realistic, and trustworthy, because of their ‘co’approach [ 28 , 29 , 33 , 34 , 39 , 40 , 42 , 43 , 46 , 48 ].

‘This experience only confirmed their view that it was important to include representatives of all the relevant professionals in the process of building a model, to make it sufficiently realistic and trustworthy, and to increase the chances of the results being accepted by them and acted upon’ [ 29 ] .

Activities used in ‘co’approaches

Authors used a range of activities, regardless of the term used for their ‘co’approach, in order to achieve the mechanisms of action discussed. It is useful to document these because often researchers rely on research methods when other activities can help to achieve these mechanisms (see Table 4 ). For example a number of studies included creative activities drawn from design, such as drawing and sketching, personas, journey maps and prototyping [ 27 , 28 , 30 , 31 , 34 , 35 , 38 , 39 , 40 , 41 , 48 , 49 , 50 ]. Some used the amalgamation of interview and focus group data to inform their ‘co’approach process [ 42 , 44 , 46 ]. Others were co-production or co-design of a whole research project [ 29 , 44 , 45 , 46 ]. Prioritisation and consensus techniques were common, including nominal group and Delphi techniques [ 27 , 31 , 34 , 37 , 43 , 47 , 48 , 49 ]. One study used a writing committee [ 43 ] and others used meetings and discussion groups [ 27 , 32 , 36 , 37 , 44 , 49 ]. Generally some form of workshop was common.

Achieving outcomes

Few of the included studies measured outcomes. Authors tended to describe the outcomes they believed they were more likely to achieve. These included more relevant research products, more usable knowledge, outputs more likely to be implemented in practice, and improved health.

More accessible, relevant and acceptable knowledge mobilisation products

Two authors perceived that their ‘co’approach helped overcome the problem of research and research findings seeming inaccessible and irrelevant to non-academic audiences [ 28 , 35 ]. Other authors felt their use of visualisations and design artefacts improved the accessibility of knowledge by simplifying complex concepts [ 28 , 30 , 35 , 39 , 50 ]. Making research and its findings more accessible and relevant was considered an important outcome [ 35 , 43 , 47 ].

‘The participation of end users in the design process ensured that the prototype was accessible to individuals of varying literacy levels with a range of cultural differences’ [ 39 ] .

Authors indicated that by using collaborative approaches they could produce more engaging, functional, practical and acceptable products [ 28 , 37 , 39 , 40 , 41 , 42 ]. Findings from user testing of prototype functionalities for an e-mental health management system supported this view [ 39 , 40 , 41 ]. Authors felt that their participatory ‘co’approach could: ‘ help ensure the end product meets everyone’s needs; improve usability; and increase engagement of users’ [ 41 ] and ‘ could result in better products that are more functional in real-life settings’ [ 40 ] .

More usable knowledge products

A number of authors felt their ‘co’approach produced outputs with potential to be useful and useable in practice [ 28 , 29 , 30 , 33 , 34 , 39 , 42 , 43 ]. Several felt that their outputs were more likely to be accepted and therefore more likely to be acted upon and used, leading to successful changes in practice [ 28 , 29 , 33 , 34 , 44 , 45 , 47 , 48 ]. Authors felt that outputs would be fit for purpose in the real world because their ‘co’approach ensured cultural and contextual factors were captured and used to inform their generation [ 28 , 33 , 34 , 40 , 43 , 48 ].

‘Including people with lived experience in guideline development can aid improved understanding of treatment options, greater involvement in health care decision making, and increased satisfaction in primary and secondary health care. This model can be used to to ultimately produce a product that has real‐world utility for patients and their families’ [ 43 ] .

Few studies carried out formal evaluation of their outputs, however data collected in four studies indicated that the process could produce useful and easy to use outputs [ 35 , 42 , 44 , 50 ].

Implementation in practice

Authors proposed that because their research was more relevant, acceptable and usable it was more likely to be implemented in practice. A number of studies provided insights into how their outputs had been implemented and impacted on clinical practice both locally and nationally [ 29 , 33 , 35 , 36 , 44 , 46 ].

“because of our adoption of the Toolbox, our implementing clinicians have assessed chronic pain in over 70% of their pediatric patients who may not have otherwise discussed their chronic pain ’’ [ 44 ].

Two casebooks used the IKT approach to ensure research outputs were more implementable [ 44 , 45 ]. Other studies found that prototypes incorporating culturally and contextually specific information had the potential to aid implementation [ 28 , 34 , 39 , 40 , 41 ]. most of the studies in this review produced outputs that required further refinement before being ready to be implemented [ 48 ].

It was acknowledged that implementation and sustained engagement with outputs was challenging. In order to achieve sustainability and long term impact after research teams departed local champions were required to continue to drive implementation forward [ 36 ].

Improved health

None of the included studies in this systematic review undertook an in depth post implementation evaluation nor did they measure or report on specific health outcomes. Many of the authors aspired to, and in some cases reported, the goal of improving healthcare outcomes and quality of care [ 28 , 30 , 34 , 37 , 43 , 46 , 50 ]. However, these claims were not based on robust evaluation data and evaluation methods were not clearly reported. A number of authors felt improving the relevance [ 40 , 41 , 43 , 46 ], acceptability [ 37 , 40 ] and usability [ 40 , 41 ] of outputs would improve outcomes or quality of care.

‘the development of a codesigned conservative model of care involving patients, clinical staff, members of the public and other stakeholders is more likely to be accepted by both providers and users, resulting in a higher rate of stakeholder satisfaction, continuous improvement and a reduced failure risk’ [ 37 ] .

Other studies demonstrated actual changes in practice as a result of introducing the co-designed outputs. These included improved consistency in clinician assessment and identification of patient problems that were previously missed [ 44 ], changes to clinical pathways [ 29 ], fewer hospital visits and admissions [ 44 ] and a reduction in the number of patients who failed to attend appointments [ 35 ]. Additional positive outcomes such as, patient satisfaction were either shown or perceived to be possible [ 33 , 43 ].

From the 24 included studies authors’ main reasons for choosing a ‘co’approach were: 1. Bringing people together. 2. valuing all knowledge. 3. To produce more relevant research products. 4. To improve health outcomes. These were achieved through several mechanisms, such as identifying and meeting all stakeholders’ needs and enabling trust and confidence in the outputs. However, there was little evidence that these approaches improved health because of the lack of robust evaluation of the interventions produced. Despite this, the findings provide useful insights into how ‘co’approaches might mobilise knowledge in health condition management and they are aligned with the five principles for co-production described by a leading research funder in the UK [ 12 ]. The NIHR [ 12 ] propose the principles of: 1. Sharing power. 2. Including all perspectives and skills. 3. Respecting and valuing all knowledge. 4. Reciprocity and 5. Building and maintaining relationships. Our review builds on these principles by highlighting activities researchers use to achieve them, further key aspects and mechanisms of action, and the relationships between them. For example, sharing of power may be facilitated if the ‘co’approach brings people together as active partners and uses creative activities. Building and maintaining relationships may be promoted by using iterative prototyping techniques. The findings from this review suggest that the process of developing adaptable, visible and tangible outputs helps participants see that their knowledge and ideas have been heard and valued. Participants may have more trust in the process and reciprocity achieved by producing relevant and acceptable outputs that meet everyone’s needs.

Langley et al.’s 2018 ‘collective making’ knowledge mobilisation model [ 70 ] specifically considers the influences of creative practices. The authors propose that their ‘collective making’ ‘co’approach influences the participants involved, the knowledge being mobilised and implementation in a number of ways [ 70 ] similar to the findings in this review. For example, influencing participants through balancing power and voice and enabling articulation of complex concepts; influencing knowledge through accessing, sharing and valuing different types of knowledge; influencing implementation through creating a sense of ownership and trust in the co-created outputs. Our review complements this model and highlights that some researchers believe similar benefits can be gained without the use of creative activities. This review demonstrates that there is no ‘one size fits all’ approach. All three ‘co’approaches, that is co-production, co-design and co-creation, were used in the studies in this review utilising a variety of activities, from research methods such as interviews and focus groups to workshops using creative activities drawn from design.

Strengths and limitations

This is the first systematic review of ‘co’approaches for knowledge mobilisation for the management of health conditions and included a large number of studies. There were however some limitations. First, there was a lack of studies that had formally evaluated the outputs of their ‘co’approach. A review focused explicitly on the effectiveness of interventions for knowledge mobilisation might have identified more relevant literature than our review. Second, the inclusion/exclusion criteria may have excluded some studies. For example, some collaborative and participatory research that could be deemed to sit under the co-production umbrella, such as studies using an IKT approach, were not included because they did not explicitly describe their approach as co-production, co-design or co-creation. The focus of this systematic review was on these three commonly used terms specifically and knowledge mobilsation. Therefore on reflection, we think that this exclusion criterion was necessary in order to make some sense of this diverse and complex field. Third, the elasticity of the term knowledge mobilisation in the healthcare literature meant the inclusion criteria for this term was broader and encompassed other terms such as knowledge exchange and evidence into practice. This meant that there was room for interpretation by the reviewers which may have led to reviewer bias. Fourth, the lack of use of MeSH terms may have reduced the number of search results meaning some potentially relevant papers may have been missed. Finally, the lead reviewer conducted the majority of the screening process and was the author or co-author of some of the included papers. The bias of the first author was minimised to some degree by working closely with a second reviewer and discussions with other authors of the review.

Conclusions and Implications for future research

This systematic review suggests that ‘co’approaches show promise in achieving successful knowledge mobilisation to improve the way health conditions are managed. However, the findings relied heavily on authors’ beliefs, with only some supporting evidence for short term outcomes such as producing acceptable outputs. There is a need for robust evaluation to ascertain the extent to which ‘co’approaches can produce improved health outcomes. A systematic review that evaluates outputs from ‘co’approaches versus those produced using alternative approaches in a diverse range of settings is recommended to assess whether the former are more likely to achieve knowledge mobilisation and improved outcomes.

Finally, undertaking research using ‘co’approaches is no easy task and it is a common criticism within the literature that authors rarely report their activities in detail nor the steps they have taken to adapt their methods to align with the key principles of ‘co’approaches [ 13 ]. The themes diagram in this review is a form of logic model [ 71 ] displaying the pathways through which ‘co’approaches might achieve desired outcomes. This could be used as a framework to help people using ‘co’approaches align their chosen activities to the key aspects and mechanisms, as identified within this review, and the principles of ‘co’approaches articulated elsewhere [ 12 , 70 ]. This will aid transparency in reporting and potentially improve an intervention’s chance of achieving successful knowledge mobilisation.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank Angie Rees information specialist at The University of Sheffield for her assistance in developing the search strategy and conducting the literature searches for this systematic review. Also Chris Redford design researcher at Lab4Living, Sheffield Hallam University for his help with figure 2 . Rights of retention statement: For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising"

This systematic review was completed as part of a University of Sheffield faculty of Medicine, Dentistry & Health, University Post Graduate Research Committee (UPGRC) Scholarship and publication funded by the University of Sheffield Institutional Open Access Fund. 

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Conceptualization: CG, AOC, LC; Review methodology: CG, AOC, LC; Search strategies: CG with review by AOC, LC; Eligibility criteria: CG, AOC, LC; Article screening: CG, EC; Pilot extraction: CG, EC; Data extraction: CG, EC; thematic synthesis: CG with review and refinement by AOC, LC, EC; Writing-original draft preparation: CG, AOC, LC. Writing-review and editing: CG, AOC, LC, EC. All authors read and approved the final manuscript.

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Grindell, C., Coates, E., Croot, L. et al. The use of co-production, co-design and co-creation to mobilise knowledge in the management of health conditions: a systematic review. BMC Health Serv Res 22 , 877 (2022). https://doi.org/10.1186/s12913-022-08079-y

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Onur Avci recognized by American Institute of Steel Construction for teaching and research in the domain of structural steel

Onur Avci

Onur Avci 's joint research on modular all-steel floor systems holds potential to speed up steel construction, earning him the 2024 Terry Peshia Early Faculty Award.

Onur Avci, civil and environmental engineering assistant professor at the  Benjamin M. Statler College of Engineering and Mineral Resources , has been recognized as this year’s Terry Peshia Early Career Faculty Award recipient from the American Institute of Steel Construction. The Terry Peshia award honors full-time, tenured faculty in civil or architectural engineering who have made significant contributions in steel research and teaching.

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Benjamin M. Statler College of Engineering and Mineral Resources

“I am pleased to be receiving this prestigious award,” Avci said. “This is a great recognition for WVU, as there has been tremendous steel research going on here. This honor certainly brings more responsibility and will fuel me to push for bigger and better things.”

Avci is the first faculty member from WVU to receive the award from AISC. The AISC’s Need for Speed initiative aims to increase design and fabrication speed in the construction of steel buildings and bridges. Avci and his team  (including faculty from WVU, Northeastern, Johns Hopkins, Virginia Tech, and Iowa State University) have developed an all-steel modular system design that eliminates the need to cast concrete for building floors, speeding up construction times and improving cost effectiveness.

“Our primary goal is to teach our students the most up-to-date design approaches,” Avci said. “This requires instructors to leave their comfort zones and update the course content to deliver the newest trends in the industry. Engineering education has been changing, and as instructors we need to keep up with it.”

Avci says that structures have become lighter and thinner in recent years, and incorporating these updates along with the vibration serviceability side of things is something that he’s been talking about in his classroom for more than a decade. Vibration and acoustics are critical considerations in construction to enhance well-being and comfort for building inhabitants. It’s estimated that global floor area will double by 2060, totaling 2.6 trillion square feet of new floor added to global stock. The new system has broad application potential, even in high seismic conditions.

The initial findings of the research project were presented at the International Modal Analysis Conference in January. Avci will be recognized by the AISC at the NASCC Steel Conference on March 20 in San Antonio, Texas.

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Industrial Chemistry & Materials

Recent progress and challenges in silicon-based anode materials for lithium-ion batteries.

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* Corresponding authors

a State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, China E-mail: [email protected] , [email protected]

b Institute of Electronics, Atomic Energy Research Establishment, Bangladesh Atomic Energy Commission, Dhaka 1349, Bangladesh

Anode materials for Li-ion batteries (LIBs) utilized in electric vehicles, portable electronics, and other devices are mainly graphite (Gr) and its derivatives. However, the limited energy density of Gr-based anodes promotes the exploration of alternative anode materials such as silicon (Si)-based materials because of their abundance in nature and low cost. Specifically, Si can store 10 times more energy than Gr and also has the potential to enhance the energy density of LIBs. Despite the many advantages of Si-based anodes, such as high theoretical capacity and low price, their widespread use is hindered by two major issues: charge-induced volume expansion and unreliable solid electrolyte interphase (SEI) propagation. In this detailed review, we highlight the key issues, current advances, and prospects in the rational design of Si-based electrodes for practical applications. We first explain the fundamental electrochemistry of Si and the importance of Si-based anodes in LIBs. The excessive volume increase, relatively low charge efficiency, and inadequate areal capacity of Si-based anodes are discussed to identify the barriers in enhancing their performance in LIBs. Subsequently, the use of binders ( e.g. , linear polymer binders, branched polymer binders, cross-linked polymer binders, and conjugated conductive polymer binders), material-based anode composites (such as carbon and its derivatives, metal oxides, and MXenes), and liquid electrolyte construction techniques are highlighted to overcome the identified barriers. Further, tailoring Si-based materials and reshaping their surfaces and interfaces, including improving binders and electrolytes, are shown to be viable approaches to address their drawbacks, such as volume expansion, low charge efficiency, and poor areal capacity. Finally, we highlight that research and development on Si-based anodes are indispensable for their use in commercial applications.

Keywords: Lithium-ion battery; Silicon-based anode; Volume expansion; Solid electrolyte interphase propagation; Binders; Composite anode materials.

Graphical abstract: Recent progress and challenges in silicon-based anode materials for lithium-ion batteries

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Generative AI fuels creative physical product design but is no magic wand

Although generative AI (gen AI) is in its infancy, the technology is already leaving an indelible mark on how physical products and packaging are conceived, innovated, and designed.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

From product packaging to car components and retail displays, gen AI  enables industrial designers to explore more ideas and product experiences, including previously unimagined ones, and develop initial design concepts significantly faster than with traditional methods.

Additionally, with the ability to visualize concepts in high fidelity much earlier in the design process, companies can elicit more precise feedback from consumers as they work to fine-tune every element of the user experience (see images below). In product research and design alone, McKinsey estimates gen AI could unlock $60 billion in productivity . 1 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023.

A comparison of six modern welding helmets rendered with generative AI. Each helmet shows a sleek, sporty aesthetic, with different design variations and transparent displays that enable welders to view key metrics and adjust light sensitivity as they work.

While gen AI tools can bring about extraordinary outputs, they cannot replace human expertise. Just as the industry saw with the arrival of computer-aided design (CAD) and later advancements such as 3-D printing and augmented and virtual reality, while the methods for designing physical products may change, design experts are needed to ensure the meaningful use of the technology and delivery of business value.

In the case of industrial design, experts conducting consumer research often unearth important insights that inspire pivotal design choices. Their skill in identifying the best concepts from the dozens of AI-generated images—assessing outputs with an eye for aesthetics and manufacturability and manipulating images based on user research and their design sense—is crucial in providing a final design that will resonate with users.

Although these tools are relatively new, our teams continue to see their significant impact on productivity. When they are used properly throughout the product development life cycle, we sometimes see a reduction upward of 70 percent in product development cycle times, providing teams with the opportunity to spend more time conducting consumer testing, refining designs, vetting suppliers, and optimizing designs for manufacturability and sustainability. These tools and processes are ultimately a vehicle for growth and innovation, enabling faster development of far better products.

But while R&D and product development leaders can use the technology today to propel innovation, they will need to understand and prepare for the technology’s limitations. In this article, we share ways gen AI can unlock creativity and productivity across the product development life cycle, examine crucial considerations for business leaders trying to create business value, and suggest steps for getting started based on our design work and the use of gen AI tools in our creative process.

Unlocking creativity and productivity across the design life cycle

When industrial designers create concepts or redesign packaging, consumer durables, experiences, spaces, or just about anything else, their creative processes generally go through a few essential phases: market and user research, concept development, and concept testing and refinement. Gen AI technology can provide tremendous value at each stage, enabling designers to iterate faster, connect the dots in new ways, and catalyze divergent thinking to create products that transform users’ everyday experiences (exhibit).

Market and user research

Almost all good physical product design starts with market research. What features or qualities are most important to potential consumers? How are consumer preferences and tastes evolving and how are our competitors responding? What gaps exist for creating a new category of offerings?

Using gen AI tools trained on large language models—such as ChatGPT, Bard, and others—designers can gather, synthesize, and make sense of existing market and consumer data far more expediently than previously possible. Moreover, because the tools draw insights from many more diverse data sources than humans alone could analyze, they can reveal untapped market opportunities and overlooked consumer needs or expectations. That enables industrial designers to build a much richer baseline of knowledge for stakeholder discussions and consumer interviews. One consumer packaged goods company augmented its market and user research with new insights from gen AI tools about consumer sentiment and how it might use its brand equity to expand into high-growth markets. With this knowledge, the design team broadened the scope of its ethnographic interviews, gaining feedback on important design elements that informed its subsequent work to develop and refine new concepts.

Concept development

As industrial designers and engineers create new product designs or iterate on the next generation of an existing product or engineering component, text-to-image gen AI tools provide a powerful medium for inspiration and innovation.

The technology’s ability to generate novel, lifelike images based on expert prompts can inspire bolder exploration and bring forward distinctive and potentially first-of-their-kind ideas. These visualizations, data, and other outputs that emerge as designers input rough sketches, ethnographic research insights, and features based on consumer sentiment into a gen AI tool can be a great starting point, drastically accelerating the concept development phase. That said, human intervention by an expert designer is still needed to validate, test, and refine outputs to make them meaningful, manufacturable, and impactful, as the images generated typically can’t be used in their initial state (for instance, some may not align with the company’s vision, others may not reflect the designer’s prompt in any meaningful way, and others still may be completely unmanufacturable).

As with previous technological evolutions, such as the emergence of CAD and 3-D printing, gen AI frees design experts from mundane and time-consuming tasks when preparing concept images, mood boards, and storyboards. By inputting iterative prompts about target performance goals and new specifications, for example, industrial designers can arrive at the “best answer” faster than if they tested different theories individually and then conducted highly manual due diligence (see images below).

Initial prompt

A generative AI rendering of a titanium bicycle pedal following an initial prompt by designer. The pedal displays numerous irregularities in the placement and number of studs, an uneven distribution and variety of structural supports connecting the top and bottom plates, and unintelligible text and logo.

Prompt progression

A series of four images depicting ten titanium bicycle pedals developed by iteratively prompting generative AI. The ten pedals display numerous design variations and flaws including inconsistencies in the size and shape of studs and the structural supports connecting the top and bottom plates, and, in one case, an axle housing that ends midway across the pedal.

Final, refined, and manufacturable

An image comparing the final raw output of a titanium bicycle pedal from generative AI following iterative prompting and the same image after it has been refined using image-editing software.  The raw image shows studs and structural supports inconsistently placed and grooves marked on inside surfaces. After refinement by the designer, the studs are uniformly positioned, the interior surfaces are smooth, and the structural supports are precisely aligned with the corners and center of the pedal for improved strength.

Industrial designers at an automotive OEM needed just two hours with the help of gen AI to create the initial design concepts for 25 variations of a next-gen car dashboard with a touch screen interface, charging surfaces, instrument panel, and other components. These concepts were then further refined by the design team using an image-editing software to provide stakeholders with a clearer picture of where the industry was going and how to evolve component interfaces, form factor, color, material, finish, and more for the latest models of electric vehicles (see images below). Without gen AI, creating images with similar detail and quality would have taken at least a week with many more iterations. This process empowered designers to bring a product experience to life in a far more tangible manner and in a fraction of the time.

Side-by-side images of a traditional car interior and a generative AI rendering. The AI rending has futuristic lighting a polished interior and larger digital display screens.

Given that gen AI outputs currently require significant manipulation, the creation of these images typically happens in the studio. But as the technology develops and its outputs become more refined, industrial designers and engineers are increasingly sitting in meetings with business leaders and conducting consumer research sessions while using gen AI tools to create inspirational images in real time based on live feedback.

Concept testing and refinement

With the ability to elevate a conceptual napkin sketch or rough design idea to an immersive visual, industrial designers can also bring new concepts and experiences to life. This can enable more meaningful discussions with business leaders and consumers as they seek feedback on potential opportunity areas, concepts, and future visions.

Executives at a preeminent museum, for instance, could better visualize opportunities to increase accessibility of museum exhibits when industrial designers edited and combined AI-generated images with supplementary visual content (sketches, graphics, and so on) to create storyboards that illustrated novel formats, products, services, and experiences (see image below).

A generative AI image of an illustration of a modern museum exhibit. People are seen looking at artwork and the image is overlayed with digital popups indicating where a viewer can click for more information or engagement.

Following the testing of initial concepts with stakeholders, designers can then use the technology to refine product style, apply finishing touches, and map future concepts to inform product road maps—sometimes in hours instead of weeks—before moving to the subsequent phases of design detailing, refinement, engineering concepts, and design for manufacturing.

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Beyond design.

Leaders seeking to further use the technology in product simulation and testing should watch the gen AI space closely. The technology is rapidly evolving, and as it does, we anticipate even more capabilities will become available to simplify the handoff between design and engineering and dramatically accelerate engineering processes. We’re already seeing the market launch of gen AI software solutions that enable industrial designers and engineers to rapidly turn product concepts into CAD models. That allows them to model products far faster and begin the engineering process more expediently. While the tools are still nascent, we can imagine in the not-too-distant future that these tools will drastically improve and accelerate design-to-engineering handovers.

We also expect to see new tools capable of rapidly analyzing designs for manufacturability and serviceability—for example, to confirm whether a product can be manufactured using a facility’s existing injection molding tools. From an engineering perspective, gen AI is already revolutionizing the way experts approach long-established simulation engineering problems, such as how to optimize the structural performance of products. One gen AI tool for finite element analysis and topological optimization—cornerstone techniques for understanding how a part performs under different conditions and how to produce lightweight yet strong structures—can generate hundreds of improved-design options for parts based on identified criteria, such as forces, pressures, and environmental conditions. In the future, we can expect an even more comprehensive range of capabilities from such tools, including the abilities to transform rough sketches into detailed engineering part models, facilitate material selection and optimization, and identify ways to enhance manufacturability, optimize components for assembly, and reduce costs.

Crucial considerations for achieving business value

Without a doubt, gen AI outputs can be impressive. However, producing meaningful outputs and turning them into a desirable, user-centric, manufacturable product that matches user preferences, pain points, and expectations doesn’t happen by just pressing a button. To achieve business value, industrial design and engineering expertise are crucial in the following areas:

  • Conducting consumer research. Consumer research gleaned from gen AI tools may seem comprehensive; however, these tools can provide incorrect information (often called hallucinations). Additionally, even when the insights provided are accurate, they can offer only a baseline of knowledge, as consumer trends and behavior often change faster than training data sets. As a result, design teams must still verify hypotheses and investigate emerging trends through primary research. By combining gen-AI-produced insights and ethnographic interviews, design teams can obtain a much richer understanding of user preferences than either can provide on their own in the same period.
  • Developing effective prompts. Highly iterative prompting is required to produce something close to what designers envision, consumers want, and companies can manufacture. A simple sentence may generate an interesting image, but the output won’t necessarily be accurate, feasible, or relevant (see images below). Ultimately, design experts must provide context for the overall concept, including subject, medium, environment, lighting, color, mood, and composition. They need to determine how much detail to include (for instance, less detail might produce more variety but result in concepts that don’t have the specific features needed). What’s more, they need to consider prompt length and how to separate complex prompts (having fewer words in a prompt means each word has more influence, which can affect outputs).

Two side-by-side illustrations of a girl painting a flower made by generative AI. The image on the left looks pretty at first glance, but upon inspection proves inconsistent with reality while the right image does not include these errors.

  • Refining gen AI outputs. Oftentimes, text-to-image tools generate flawed images: a rogue plant grows out of the top of a television, or an unflyable drone is created (see image below). Organizations should expect to perform substantial postproduction editing—for instance, by using image-editing software to fine-tune the colors, fonts, and patterns used in the final concepts—to achieve a meaningful result. Even when initial outputs look as though they could be on store shelves today, closer inspection typically finds they are a far cry from a manufacturable product. Today, designers and engineers must still create their refined version of a concept in CAD to ensure the product accounts for manufacturing specifications, requirements, and constraints.

A generative AI rendering of a passenger drone with elements insufficient for safety and manufacturing.

  • Curating the best concepts. Gen AI can produce dozens of concepts quickly, but as the famous “jam experiment” study showed, too many choices can overwhelm both important stakeholders and consumers. As a result, organizations will need design experts to identify the best ideas from the large number of images produced and refine them based on aesthetics, feasibility, fit for use, and more to ensure concept testing with users yields valuable feedback.
  • Adding a good dose of human empathy. AI tools are only as good as the data they are trained on. And given the “averaging” that may occur with aggregated inputs, they can perpetuate historical biases, oversimplify solutions, and gloss over insightful bits of nuanced human behavior that can provide the seeds for innovation. Industrial designers and engineers, therefore, must provide ongoing oversight of the design, making certain that all facets of product use are considered—from the aesthetics (whether the design is aligned with regional and cultural preferences) to ergonomics (whether the gen AI output is too large or unwieldy for the target audience) and usability (for instance, whether the product is accessible for individuals with disabilities).

Getting started

Adding gen AI to the physical product design tool kit can accelerate and advance product design innovation, but only if teams can effectively use the technology. Based on our work and experience using the tools, we recommend R&D and product leaders consider the following actions to begin building their gen AI capabilities:

  • Set aside time for learning and exploration. This action can involve empowering teams to test the technology in commonplace activities, such as iterating on new product features for an existing offering. It should also involve providing opportunities, such as a dedicated messaging channel or team meetings, to share successes and challenges. In other areas, such as software development, McKinsey research has found that the more practitioners use the tools and share their learnings with others, the better they get . We find the same is true in physical product design.
  • Identify and launch a pilot in high-value domains. While it can be tempting to apply the technology to every project under way, leaders are best served by identifying a pilot project where there’s potential to generate considerable value. A pilot project could use gen AI across the design life cycle for a signature product, or it could focus on streamlining one process, such as research, across its entire flagship product line.

Evaluate risks and institute guardrails. Gen AI introduces new legal, ethical, and reputational risks that leaders must carefully consider and manage. These include concerns about data security (whether confidential information is being exposed when prompting the tool), intellectual property (whether the model outputs infringe on copyrighted, trademarked, patented, or otherwise legally protected material), and reliability (whether the tools are hallucinating and providing inaccurate responses to prompts), among others. In certain instances, such as gen AI’s capacity to hallucinate, the risks may be limited, as design experts typically vet and verify information provided by the tools and marry it with additional primary data sources. Furthermore, any surreal and fictitious image generated by the tools during concept development may be an asset, inspiring greater creativity and originality.

In other instances, especially those related to intellectual property rights and data security, action is required to ensure the responsible use of the technology. Leaders should review their legal processes and design standards to confirm they have the necessary diligence measures in place to ensure a final product doesn’t improperly reproduce third-party intellectual property, regardless of where their teams draw inspiration from—be it gen AI tools or their own research on- and offline. (In cases where teams wish to share AI-generated images they produce as is, leaders should ensure they understand intellectual property and ownership terms put forth by different tooling vendors as well as any relevant local laws that may govern ownership of an AI-generated output.)

Leaders should also implement policies that guide teams on what information can and cannot be used in gen AI prompts. Some best practices include understanding the terms of service for the given gen AI tool and refraining from using third-party intellectual property, proprietary insights, or other sensitive information in prompts.

  • Educate business stakeholders on new processes. The level of detail and refinement of AI-generated images can create the impression that a product is much closer to completion than it is. As a result, as R&D organizations adopt these tools, they should be transparent about their use and provide stakeholders with a clear understanding of what the images represent, their use, and their limitations. Regular updates about the actual progress of a project can also ensure that the highly realistic visual representations don’t lead to overoptimistic expectations.
  • Upskill industrial designers for future roles. Using gen AI in physical product design will invariably create new roles wherein design experts become “curators of creativity,” linking, manipulating, and drawing inspiration from the technology’s outputs to solve product challenges. This role requires storytelling and human-centered design skills, manufacturing know-how, competencies in other digital tools (such as CAD, illustration, sketching, and rendering software), a deep understanding of the use of different materials in design, and so on. It can take years to master these skills and understand how and when to pair with gen AI tools; as such, leaders should begin upskilling their teams today.

Gen AI has begun to reshape physical product design, enabling industrial designers to be more productive, creative, and strategic in building products that solve user needs. While the technology’s outputs can be dazzling, its ability to create business value becomes apparent only when combined with the skilled hands and discerning eyes of design experts. As adoption gains speed and as more designers and engineers integrate this technology into their workflows, we could see some genuinely revolutionary design and engineering solutions blossom. This will potentially lead to an entirely new aesthetic era with ingenious form factors, greater efficiency in material usage and manufacturability, and improved product life spans—benefiting both the companies that create these products and the people who use them.

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ORIGINAL RESEARCH article

Current knowledge of human mpox viral infection among healthcare workers in cameroon calls for capacity-strengthening for pandemic preparedness.

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  • 1 Chantal BIYA International Reference Centre for Research on HIV/AIDS Prevention and Management (CIRCB) Yaoundé, Cameroon
  • 2 Faculty of Medicine and Surgery, University of Rome “Tor Vergata”, Rome, Italy
  • 3 Faculty of Sciences and Technologies, Evangelical University of Cameroon, Bandjoun, Cameroon
  • 4 Faculty of Medicine, UniCamillus - Saint Camillus International University of Health Sciences, Rome, Italy
  • 5 National AIDS Control Committee, Central Technical Group, Ministry of Public Health, Yaoundé, Cameroon
  • 6 Faculty of Health Sciences, University of Buea, Buea, Cameroon
  • 7 National Public Health Emergency Operations Coordination Centre, Ministry of Public Health, Yaoundé, Cameroon
  • 8 Faculty of Medicine and Biomedical Sciences, University of Yaoundé I, Yaoundé, Cameroon
  • 9 Department of Health Biotechnology, Fobang Institutes for Innovations in Science and Technology, Yaoundé, Cameroon
  • 10 Faculty of Health Sciences, University of Bamenda, Bamenda, Cameroon
  • 11 Laboratory of Microbiology, University of Yaoundé I, Yaoundé, Cameroon
  • 12 Elisabeth Glaser Peadiatric AIDS Foundation (EGPAF), Douala, Cameroon
  • 13 Helen Keller International, Bafoussam, Cameroon
  • 14 Faculty of Sciences, Department of Microbiology, University of Yaoundé I, Yaoundé, Cameroon
  • 15 School of Health Sciences, Catholic University of Central Africa, Yaoundé, Cameroon
  • 16 Faculty of Sciences, University of Buea, Buea, Cameroon
  • 17 Faculty of Medicine, University of Antwerp, Antwerp, Belgium
  • 18 Department of Disease, Epidemics, and Pandemics Control, Ministry of Public Health, Yaoundé, Cameroon
  • 19 Division of Health Operational Research, Ministry of Public Health, Yaoundé, Cameroon
  • 20 Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia
  • 21 Microbiology, IRCSS Bambino Gesu' Pediatric Hospital, Rome, Italy

Introduction: An increased incidence of human Monkeypox (Mpox) cases was recently observed worldwide, including in Cameroon. To ensure efficient preparedness and interventions in the health system, we sought to assess the knowledge of Mpox's transmission, prevention, and response among healthcare workers (HCWs) in Cameroon.

Methods: A cross-sectional online survey was conducted among HCWs in Cameroon using 21-item questions adapted from the United States Centers for Disease Control and Prevention (US-CDC) standard questionnaire on Mpox. The overall knowledge of Mpox was assessed by cumulative score and categorized as excellent (≥80%, 17/21) or good (≥70%, ≥15/21) knowledge. The regression analysis was used to identify the predictors of Mpox knowledge.

Results: The survey enrolled 377 participants, but only responses from 342 participants were analyzed. Overall, 50.6% were female participants, and 59.6% aged 30 years or younger. The majority of the participants were medical doctors (50.3%); most worked in central-level hospitals (25.1%) and had 1–5 years of experience (70.7%). A total of up to 92.7% were aware of Mpox, with social media (58.7%) and radio/television (49.2%) as the main sources. The mean knowledge score was 14.0 ± 3.0 (4 to 20), with only 12.9% having excellent knowledge (≥80%) and 42.1% having good knowledge of Mpox. Younger age (26–30 years old) was associated with good knowledge, while workplace type was associated with excellent knowledge of Mpox (aOR [95% CI]: 4.01 [1.43–11.24]). Knowledge of treatment/management of Mpox was generally poor across the different professional categories.

Conclusion: Knowledge of Mpox among HCWs is substandard across different professionals. Thus, for optimal preparedness and immediate interventions for Mpox and similar emerging pathogens, capacity-strengthening programs should be organized for HCWs while encouraging scientific literature and organizational social media websites.

1 Introduction

Human monkeypox (Mpox) is a viral zoonosis caused by the monkeypox (Mpox) virus belonging to the orthopoxvirus genus of the Poxviridae family (the same family as the virus that caused smallpox, which has now been eradicated). The virus is endemic in West and Central Africa, where it is thought to exist primarily in different types of rodents. There are two groups or “clades” of Mpox, one found in the Congo Basin of Central Africa with a case fatality of up to 10% and the other in West Africa with a case fatality rate of < 3% ( 1 , 2 ).

Mpox can be transmitted via direct contact with infected body fluids, sexual contacts, lesion material from humans or animals, or indirect contact with contaminated material ( 3 , 4 ). Human-to-human transmission occurs primarily through large respiratory droplets ( 5 ). The symptoms include fever, headache, malaise, muscle aches, swollen lymph nodes, and proctitis ( 6 ), followed by a rash a few days later that begins on the face and spreads to other parts of the body. The complications of monkeypox infections include secondary infections, bronchopneumonia, sepsis, encephalitis, and infection of the cornea with ensuing loss of vision. The illness can last up to 4 weeks but starts to fade when the skin lesions begin to subside ( 7 ). The virus is known to evade detection by the inhibition of the host antiviral immune response (antiviral chemokines, cytokines, and antigen presentation) and the suppression of the activation of T-cells ( 8 ).

Mpox was first identified in 1958 during an outbreak of Mpox in the Asian monkey Macaca fascicularis , which was used for polio vaccine research at an animal facility in Copenhagen, Denmark ( 9 ). The first Mpox case in humans was reported in the Democratic Republic of Congo (DRC, previously known as Zaire) in 1970, and the disease has remained endemic in the country and other African countries ( 2 ).

Since 2016, cases have appeared in the Central African Republic, Liberia, Nigeria, and Sierra Leone( 1 ). In 2017, the largest outbreak of Mpox was reported in Nigeria, with 197 suspected cases and 68 confirmed cases, and by the end of 2018, the number of confirmed cases increased to 89, with a case fatality rate of 6.7% ( 1 , 10 ). Human Mpox cases have also been previously reported in the United States in June 2003 ( 11 , 12 ), in the UK ( 13 ) in September 2018, and in Israel ( 14 ) on 4 October 2018. In the case of the United States, Mpox was transmitted from infected native prairie dogs that were housed with infected exotic pets imported from Africa ( 11 , 12 ), while in the UK ( 13 ) and Israel ( 14 ), patients were travelers who had returned from Nigeria.

As an epicenter or endemic country for Mpox, the Democratic Republic of the Congo conducts routine Mpox surveillance and clinical trials on potential Mpx vaccines among HCWs ( 15 , 16 ). One of the important aspects of the surveillance system is to enhance the capacity of healthcare workers (HCWs) to identify and report cases and improve patient management ( 16 ). For an optimal response strategy, HCWs, particularly medical doctors and nurses, should have knowledge about the transmission patterns and clinical symptoms of Mpox to be able to quickly identify, report, and manage new cases to prevent further community-related or nosocomial transmission.

The Africa CDC outbreak brief on the MPox pandemic in January 2023 indicated that between January 2022 and January 2023, 1,296 cases and 228 deaths (CFR: 17.6%) in 13 African Union (AU) member states were reported. These countries include Cameroon (18 confirmed cases; 3 confirmed deaths), Benin (3 confirmed cases;0 confirmed deaths), Central African Republic (CAR) (13 confirmed cases;3 confirmed deaths), Congo (5 confirmed cases;3 confirmed deaths), the Democratic Republic of Congo (DRC) (319 confirmed cases;204 confirmed deaths), Ghana (116 confirmed cases;4 confirmed deaths), Liberia (6 confirmed cases;0 confirmed deaths), Nigeria (756 confirmed cases;7 confirmed deaths), Egypt (4 confirmed cases;0 confirmed deaths), Morocco (3 confirmed cases;0 confirmed deaths), Mozambique (1 confirmed cases;1 confirmed deaths), South Africa (5 confirmed cases;0 confirmed deaths), and Sudan (18 confirmed cases;1 confirmed deaths) ( 17 ).

In Cameroon, between 30 April and 30 May 2018, a total of 16 suspected cases (1 confirmed and 15 suspected cases) were reported to the Department of Disease, Epidemic and Pandemic Control of the Ministry of Public Health ( 18 ). These cases were identified in five health districts (HD) within five regions of Cameroon, namely, Njikwa HD ( n = 6 suspected, n = 1 confirmed), Akwaya HD ( n = 6 suspected), Biyem-Assi HD ( n = 1 suspected), Bertoua HD ( n = 1 suspected), and Fotokol HD ( n = 1 suspected), with newer hot spots identified in other geographical locations, particularly, in the South West region ( 18 ). To mitigate this emerging global threat at the country level, the government of Cameroon developed and implemented a public health response strategy, which included the training of HCWs on infection prevention and control (IPC), with emphasis on the use of personal protective equipment, hand hygiene, and physical distancing, where necessary. Information related to the isolation of cases, symptomatic case management, and hand-washing techniques has been shared widely by the IPC workforce within hot spots and high-risk settings ( 19 ). A recent outbreak was reported in Cameroon in September 2022, in the South West region ( 20 ), and as of 19 April 2023, Cameroon had recorded 106 suspected cases, 18 confirmed cases, and 3 deaths related to Mpox ( 21 , 22 ). These confirmed cases were found in four out of the five regions (South, Centre, North West, and South West), which called for the strengthening of the response strategy to stop its spread ( 23 ).

The increased number of human Mpox cases demonstrates the need and the importance of IPC, early detection, quick response, and the management of disease from HCWs. A report by the WHO and Africa CDC showed that one of the challenges faced in preventing the re-emergence of Mpox is the lack of sufficient knowledge about Mpox among HCWs in several countries, including high- and low-income settings ( 2 ).

Shafaati et al. ( 8 ) emphasized the importance of awareness and training campaigns to address the risks of sexual transmission of Mpox and prevent stigmatization of certain groups. A recent cross-sectional study assessing Mpox knowledge and attitudes of HCWs in some hospitals in Southern Italy in 2022 reported an unsatisfactory knowledge assessment, with a reported mean score of only 3.4 (0–9) ( 24 ). Furthermore, in a systematic review conducted by Mohamed L. and Abanoub A. in 2022, the overall knowledge of Mpox was unsatisfactory among nine articles, especially when assessing the knowledge of Mpox in Europe, the Middle East, and Asia ( 25 ). More precisely, poor knowledge of Mpox can lead to a large circulation of undiagnosed infections and thus skew epidemiological trends in resource-limited settings (RLS). Hence, in order to support the national response against Mpox at the country level, we sought to assess the knowledge of Mpox's transmission and management among HCWs in Cameroon.

2.1 Study design and settings

Within the framework of the country's response to Mpox, a cross-sectional online survey was conducted from August to October 2022 to assess the knowledge of Mpox viral infection among HCWs who are on service within the health system in Cameroon. The design and setting of this study were based on previous studies ( 26 – 28 ).

To achieve our intended goal, we used a random sampling method (self-administered online survey). According to Cameroon's Ministry of Public Health, the country has 39,720 health workers ( 29 ). Considering a 5% margin of error and a 95% confidence interval, a minimum of 381 participants were needed for this study. To ensure diversity, target participants, mainly medical doctors, nurses, and other HCWs (pharmacists, dermatologists, laboratory scientists, and nursing assistants) working at various levels of the healthcare system (central-level hospitals, district hospital (primary healthcare facilities), medicalized health centers, private hospitals, and other types of health facilities) were selected. The recruitment strategy involved reaching out to healthcare workers through social media, emails, and professional networks. Efforts were made to ensure diversity and representation by direct phone calls for participation and targeting underrepresented groups where necessary. We acknowledge that online surveys in Knowmedge, attitude and practice (KAP) studies are susceptible to some inherent biases including self-selection, non-response, social desirability, recall, sampling, access, and misinterpretation biases. These biases might have led to an unrepresentative sample, inaccurate responses, and underrepresentation of certain groups. To mitigate these potential biases, we used standardized assessment tools and provided clear instructions to minimize subjective interpretation. The Cameroonian health system has a National Public Health Emergency Coordination Centre with strategic and operational plans in response to infectious diseases of epidemics and pandemic potentials, including COVID-19, Cholera, Mpox, and viral hemorrhagic fevers. Field activities were conducted with the interventions of several stakeholders with a multi-sectorial approach in every hot spot and high-risk geographical location.

2.2 Survey instrument

A pre-tested and standard questionnaire was developed before the commencement of the study. The questionnaire consisted of questions to assess knowledge of Mpox and to collect a range of potential explanatory variables, with a total of 21 item multiple choice questions which were adapted from the United States Centers for Disease Control and Prevention (CDC) questionnaire ( 30 ) (see Appendix ). The questionnaire was developed in both English and French, which are the two official languages of Cameroon. For maximal efficiency (validation), pre-testing (pilot) was performed among 20 independent HCWs who were not included in the study (10 medical doctors, 5 nurses, and 5 clinical laboratorians). The questionnaire was then finalized and validated using various feedback obtained from the pilot testing phase. After administering the survey with this pilot group of respondents and repeating the survey with the same group at a later point in time, there was a complete agreement (reliability) between the two time points (kappa = 1). The questionnaire content validity was approved by a majority of independent HCWs (90%, 18/20).

2.3 Data collection

Invitation to complete the anonymous online survey was sent using social media (mainly WhatsApp) or e-mails. Efforts were made to ensure the participation of HCWs from the rural areas, especially in the southern region where people were sensitized during meetings to take up the survey, and up to two reminders were sent after the initial message. The questionnaire entailed detailed features and contacts of the principal investigators for any further clarification, as well as the purpose of the study for informed consent prior to enrolment. The survey was estimated to take ~7–10 min to complete and without using any documentation. As the selection criteria, this study was limited to only active Cameroonian HCWs practicing in Cameroon, and those who were willing to participate and completed the questionnaire in ≤ 10 min without using any documentation were retained for analysis. The participants who fell short of the aforementioned requirements, as well as those who submitted incomplete responses, who submitted duplicate answers, with inconsistencies in their answers, and whose variables for assessing their level of knowledge were not clearly defined, were excluded from the study.

To ensure confidentiality, the names of the participants were not collected, and only the principal investigator had access to the survey account. At the end of the survey period, the raw data were extracted and imported into statistical software for analysis. Data were protected using specific anonymous and unique identifiers with a password-protected computer. To control and avoid resubmission, duplication, or multiple participation, we used unique identifiers such as email addresses or participant IDs. The study fulfilled the CHERRIES criteria ( 31 ).

2.4 Study variables

The response variable in this study was the knowledge of Mpox viral infection among HCWs in Cameroon. The questionnaire included knowledge of Mpox transmission, clinical features, and treatment/management. The questionnaire consisted of a 21-item questionnaire in which a correct response was scored one (1) and an incorrect response was scored zero (0). The scores were summed to give a total score ranging from 0 to 21. Two different cut-off scores were defined: ≥80% (at least 17/21) and ≥70% (at least 15/21), representing excellent and good knowledge of Mpox, respectively. Although previous studies used Bloom's cut-off point of 80–100% as good scores, 60–79% as moderate scores, and < 60% and below as poor scores ( 32 ), our team decided to create two subdivisions instead of three. Here, we chose to use two scenarios based on the 80% and 70% thresholds and considered scores < 70% as indicative of poor knowledge of Mpox. This decision was made to better distribute the survey's scores into more distinct categories given the volume of questions.

To facilitate the analysis and interpretation of data, we operationalized variables into specific categories and ranges. Four main groups of explanatory variables that could affect knowledge were categorized and assessed: sociodemographic characteristics, workplace characteristics, the characteristics of the medical specialty, and exposure to and/or sources of Mpox-related information. According to the distribution of participants, age was categorized into four specific ranges (20–25, 26–30, 31–39, and ≥40 years). The medical profession, defined as the completed/graduate medical or paramedical training, was grouped into the following: medical doctors, nurses, and other HCWs, which represent the three main categories of health workers in Cameroon. Workplace characteristics included the types of health facilities: central-level hospitals, district hospital (primary healthcare facilities), medicalized health centers, private hospitals, and other health facilities which represent the Ministry of Public Health's classification of health facilities. To assess the characteristics of the medical professionals, information on HCWs' job locations (rural or urban), their professional experiences (1–5, 6–10, 11–15, and ≥16 years), and whether they had attended any continuous education or training (local, national, and international conferences in the last 5 months) were collected. To assess exposure to or sources of Mpox-related information, the respondents were asked whether they had ever received Mpox information during their professional training and whether they had heard about Mpox prior to the interview. This categorization allowed for the capture of meaningful differences within these characteristics.

2.5 Statistical analysis

Frequencies, proportions, and confidence intervals were computed, and data were summarized using tables and figures. The associations between the explanatory variables and the dependent variables were assessed using a two-step logistic regression model for both ≥70% and ≥80% cut-off scores, representing good and excellent Mpox knowledge, respectively. Initially, all explanatory variables were analyzed separately in a univariate model, and variables with a p -value of ≤ 0.25 were then included in the multivariable logistic regression analysis to assess the impact of multiple independent variables on the likelihood of good knowledge of Mpox. Good knowledge of Mpox was the baseline variable used for comparison (outcome), and specific variables were chosen for inclusion based on their theoretical relevance to the outcome and existing evidence of their association with good knowledge of Mpox. For comparison, females were used as the reference for the “gender” variable; young HCWs aged 20–25 for the “age” variable, medical doctors for the “medical profession” category, the central hospital (tertiary healthcare facilities) for the “level of health facility”, and HCWs with 1–5 years of experience for “years of experience” category.

To ease result interpretations, the estimated crude odds ratio (OR) of unadjusted analyses and the adjusted OR (aOR) were interpreted in relation to a reference category. The significance was assessed at p = 0.05, and analyses were conducted using Statistical Package of Social Sciences version 22.0 software (SPSS Inc., Chicago, IL, USA).

2.6 Ethical considerations

In accordance with the Declaration of Helsinki on good clinical practices and ethical considerations, the present study was approved within the frame of multisectoral surveillance and in response to public health emergencies of zoonotic origin (authorization Ref. N° E2–168/L/MINSANTE/SG/DLMEP/SDLEP from the Ministry of Public Health in Cameroon). Prior to enrollment, the study information sheet was provided to each potential participant, and informed consent was then obtained from each participant. Data confidentiality and privacy of participants were ensured by the use of anonymized unique identifiers, and the data were secured in an encrypted password-protected computer. Only authorized individuals, such as the principal and co-principal investigators, had access to the survey account. The generated data were used to strengthen the capacity of the target population on better outbreak preparedness and response through result dissemination and exploitation.

3.1 Respondents' characteristics

During the survey, a total number of 377 responses were received from study respondents, but 35 were excluded due to incomplete information and longer or shorter time of completing the questionnaire (i.e., < 5 min to mitigate the risk of bias or more than 15 min to limit events of answers following consultations of information from different sources before responding). Respondents were expected to complete the questionnaire between 7–10 min. The final analysis included 342 (90.7%) respondents, which represents ~90% (342/381) of the participation rate for the minimum sample size, with a margin of error of 5.3%. The characteristics of the surveyed HCWs are presented in Table 1 .

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Table 1 . Factors associated with an excellent knowledge (80% threshold) of human Mpox infection among HCWs.

Of the participants enrolled, 8 of the 10 regions of Cameroon were represented. More specifically, 42.6% (146/342) were from Yaounde, 11.9% (41/342) were from Douala, 10.8% were (37/342) from Bafoussam, 9.4% (32/342) were from Ngaoundere, 8.7% (30/342) were from Buea, 7.8% (27/342) were from Bertoua, 6.4% (22/342) were from Ebolowa, and 2.0% (7/342) were from Garoua. More than half of the participants, i.e., 172 (50.3%), were medical doctors. Concerning the gender of the participants, 50.6% (173/342) were female participants; for age, 59.6% were 30 years old or younger. Approximately 25.1% (86/342) of the respondents worked in central-level hospitals, 23.7% (81/342) in medicalized health centers, 10.2% (35/342) in private hospitals, and 27.5% (94/342) in other health facilities (research centers and non-governmental organization). Most of the HCWs (70.7%, 242/342) had a professional experience between 1 and 5 years ( Table 1 ).

3.2 Source of information

In this study, 92.7% (317/342) of the participants reported having heard about Mpox infection; of these, 58.7% (186/317) of them received their information from online media, and 49.2% (156/317) of them received their information from radio/television. Furthermore, 30% (95/317) of the participants gained their information during their medical training, 24% from colleagues, 13.2% from peer-review articles, 17.7% from newspapers or magazines, 18.6% from national or international conferences, and 12.3% from other sources ( Figure 1 ).

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Figure 1 . Sources of information on Mpox among HCWs. Some HCWs obtained Mpox information from multiple sources.

3.3 Knowledge of mpox and associated determinants

The median score on Mpox knowledge was 14 (95% CI: 13–15), and the score ranged from 4 to 20. Using the 80% cut-off score, only 44 (12.8%) out of 342 respondents had an excellent knowledge of Mpox. When the cut-off was reduced to 70%, 42.1% (144 out of 342) of respondents had a good knowledge.

Across some domains, the majority of the respondents had accurate knowledge of Mpox. For example, most (91.8%; 314/342) respondents stated that Mpox is caused by a virus, and more than 80% of them stated that Mpox and smallpox have similar signs and symptoms. Approximately 36.1% (218/342) of the respondents stated that some human Mpox cases were detected in Cameroon. Assessing respondents' “knowledge on[sic] transmission,” those in the “Others” category [68.3% (67/98)] had poor knowledge of human-to-human transmission ( Figure 2 ). Concerning the zoonotic transmission of Mpox, the majority of participants had at least a good knowledge of ≥70% ( Figure 2 ). Participant's knowledge of clinical features was generally good (≥70%) ( Figure 3 ). However, no professional category had a good knowledge of the presence of vesicles and papules, which are key clinical features of Mpox ( Figure 3 ). Knowledge of treatment/management was generally poor across the different professional categories (< 70%) ( Figure 4 ).

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Figure 2 . Knowledge of Mpox transmission means. “Others” (laboratory scientists, epidemiologists, pharmacy technicians, radiographers, physiotherapists, and dental technicians); “Overall” (mean of knowledge among medical doctors, nursing, and other categories); Q07 and Q08 represent questions 07 and 08 in the questionnaire used to assess the level of knowledge; Q07: Monkeypox is easily transmitted from human-to-human. Q08: Monkeypox could be transmitted through a bite of an infected monkey. GK, Good Knowledge (70% of good response).

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Figure 3 . Knowledge of Mpox clinical diagnostics among HCWs. “Others” (laboratory scientists, epidemiologists, pharmacy technicians, radiographers, physiotherapists, and dental technicians); “Overall” (mean of knowledge among medical doctors, nurses, and Others categories); Q10, Q11, Q12, Q13, Q14, Q15, and Q16 represent questions 10 to 16 in the questionnaire used to assess the level of knowledge; Q10: Monkeypox and smallpox have similar signs and symptoms. Q11: Monkeypox and smallpox have the same signs and symptoms. Q12: Flu-like syndrome is one of the early signs or symptoms of human Monkeypox. Q13: Rashes on the skin are one of the signs or symptoms of human Monkeypox. Q14: Papules on the skin are one of the signs or symptoms of human Monkeypox. Q15: Vesicles on the skin are one of the signs or symptoms of human Monkeypox. Q16: Pustules on the skin are one of the signs or symptoms of human Monkeypox. GK, Good Knowledge (70% of good response).

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Figure 4 . Knowledge on treatment/management of Mpox among HCWs. “Others” included laboratory scientists, epidemiologists, pharmacy technicians, radiographers, physiotherapists, and dental technicians. Q18, Q19, and Q20 represent questions 18 to 20 in the questionnaire used to assess the level of knowledge. Q18: One management option for patients with Monkeypox who are symptomatic is to use paracetamol. Q19: Antivirals are required in the management of human Monkeypox patients. Q20: Antibiotics are required in the management of human Monkeypox patients. GK, Good Knowledge (70% of good response).

The association of Mpox knowledge and some explanatory variables was assessed using both cutoff scores (i.e., 70% and 80%). Using the 80% cutoff score, at the univariate level, the age group of 31–39 years (17.3%) and the “Others” type of workplace were associated with excellent knowledge (OR:4.82 [95% CI:1.0–4.6s], p = 0.041; and OR:3.05 [95% CI:1.21–7.63], p = 0.017, respectively) compared to those aged 20–25 years and those who worked in central-level hospitals, respectively ( Table 2 ). However, the multivariable analysis showed that the “Others” professional category (OR: 0.32 [95% CI: 0.26–0.82], p = 0.018) and the “Others” type of workplace category (OR: 4.01 [95% CI: 1.43–11.24], p = 0.008) were independently associated with excellent knowledge of Mpox.

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Table 2 . Factors associated with good knowledge (70% threshold) of human Mpox infection among HCWs.

With the lower cut-off score (70%), the age groups 26–30 (46.6%) and 31–39 years (41.8%) were associated with good knowledge of Mpox (OR: 2.63 [95% CI: 1.20–5.40], p = 0.009; and OR: 2.1 [95% CI: 1.0–4.6], p = 0.04, respectively), when compared to those aged 20–25 years. However, in the multivariate analysis, only the age group 26–30 years was associated with a good knowledge of Mpox (OR: 2.74; 95% [CI: 1.2–5.8], p = 0.008) when compared to the age group 20–25 years.

4 Discussion

Responding to outbreaks, such as Mpox, requires a strong collaboration between all stakeholders, including frontline healthcare workers. In Cameroon, both event-based and case-based surveillance are put in place, but the current surveillance system mainly relies on case-based surveillance. Therefore, it is paramount that HCWs (particularly medical doctors and nurses) get a good mastery of the knowledge and case definitions and the management of potential epidemic diseases. This is because they are responsible for the early detection and management of cases at health facility levels. For this reason, our study aimed to assess the knowledge of HCWs in Cameroon on the ongoing Mpox infection, considering the transmission, clinical features, and management/treatment of the infection.

Data generated from this study revealed that, in general, the knowledge of HCWs on Mpox in Cameroon was poor (42%). Less than 15% of the participants were able to answer correctly to 80% of the 21 questions. When looking at some of the factors associated with knowledge of Mpox at an 80% cut-off score, we found that HCWs other than medical doctors and nurses had especially poor knowledge of Mpox. It was worrisome to observe that < 20% of medical doctors and nurses recorded an excellent understanding because they are directly involved with patient care.

It was interesting to note that those in the categories of other health facility levels, including research centers and non-governmental organizations (NGOs), showed a slightly higher knowledge than those in hospital settings, which might be partly justified by the fact that several respondents in this category are involved with the design or implementation of public health policies related to the Mpox response. It was, for example, reported that public health NGOs have specific missions, with most largely embodying epidemiological surveillance of infectious diseases, which perhaps exposes them more to new emerging and re-emerging health conditions ( 33 ). The other variables including age, gender, and the number of years of work experience did not seem to show a significant difference in the Mpox knowledge. This finding indicates a uniformly low level of Mpox knowledge across these variables. This low knowledge of Mpox among HCWs is not only limited to Cameroon, as a previous study found a uniformly low knowledge among general practitioners in Indonesia ( 34 ). Moreover, a cross-sectional study conducted in 2022 to assess the knowledge and attitudes of HCWs in some hospitals in Southern Italy reported unsatisfactory knowledge ( 24 ). A systematic review by Mohamed L. and Abanoub A. showed that the overall understanding of Mpox was poor among nine articles, which exclusively assessed Mpox knowledge in Europe, the Middle East, and Asia ( 25 ). As Mpox was a rare disease, it received less attention. The recent pandemic of Mpox spread faster at a large scale and affected the most vulnerable populations, therefore indicating that more attention should be given to it.

In the present study, even at a threshold of 70% (here referred to as good knowledge), < 50% of the participants had good knowledge. Most of the participants, including medical doctors, had poor knowledge (< 70%) of the evolution and presentation of the classic clinical features of Mpox and case management. It should be noted that most of the HCWs who participated in this study were still in their early career, with only 1–5 years of working experience, which could have impacted their poor knowledge.

An exploratory analysis based on the cut-off score knowledge of 70% was equally carried out. A multivariable analysis indicated that those aged 26–30 years had a higher level of knowledge (47%) than those in other age groups. The age group of 26–30 years is part of the social media-friendly group; consequently, they might be more likely to get Mpox-related information. Of note, ~58% of the participants reported receiving information about Mpox via online media platforms (Facebook, WhatsApp, podcast, etc). It was reported elsewhere that young HCWs tend to prefer to consult social media networks for information because of their rapid accessibility ( 35 ). Despite some information lacking validity, social networks have the particularity of transmitting data in record time and with a larger coverage. In this digital era, social media can represent an effective communication channel that can provide continuous education to HCWs ( 36 ). There was uniformly low knowledge of Mpox, considering other variables such as gender, type of workplace, work experience, and medical training. This finding suggests that, in such a context, the infection can spread unnoticed in the community without being detected/reported timeously. Therefore, strategies for enhancing the knowledge of HCWs on the detection and management of zoonotic Mpox are needed, including sensitization of HCWs via online platforms to respond adequately to such outbreaks ( 37 ). These strategies are particularly important as they resonate with the One Health approach for sustainable infection prevention and control ( 38 ).

In the frame of pandemic preparedness and interventions, considering the reported pitfalls among HCWs would guide global health agencies (WHO, Africa CDC, etc) in tailoring capacity-building or strengthening programs for optimal efficiency in epidemic/pandemic preparedness and response at the continental and global levels.

This study has some limitations. This was an online survey that required an internet connection; as such, there was a potential selection bias in relation to the availability of internet access, especially in rural areas ( 39 ).

5 Implications and recommendations

The study's findings highlight the critical need for targeted training programs to enhance healthcare workers' (HCWs) understanding of epidemic diseases, such as Mpox, particularly among medical doctors and nurses. The uniformly low level of Mpox knowledge across various demographic and professional variables highlights the potential impact on outbreak response and the urgent need for comprehensive capacity-building efforts. To address these challenges, it is recommended to leverage coordinated social media and online platforms for continuous education and sensitization of HCWs, considering their accessibility and potential to reach a wider audience. In addition, there is a need to conduct representative studies to ensure a comprehensive understanding of HCWs' knowledge levels nationwide (to overcome potential selection biases related to internet access, especially in rural areas), thereby guiding the development of capacity-building initiatives and pandemic preparedness strategies. These implications and recommendations are crucial for guiding the development of capacity-building initiatives and pandemic preparedness strategies at both national and global levels.

6 Conclusion

Knowledge of Mpox among HCWs within the health system of Cameroon is uniformly low across sociodemographic, workplace, and medical professional characteristics. Thus, for optimal preparedness and interventions on IPC, case management, and surveillance of Mpox and similar emerging pathogens, capacity-strengthening programs should be reinforced in the Cameroonian context and similar settings, with a particular focus on HCWs in clinical facilities and the older adults, while encouraging scientific literature and organizational social media web sites. Such evidence-based interventions could also support response in several African settings.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements. In accordance with the Declaration of Helsinki on good clinical practices and ethical considerations, the present study was approved within the frame of multisectoral surveillance and in response to public health emergencies of zoonotic origin (authorization Ref. N° E268/L/MINSANTE/SG/DLMEP/SDLEP from the Ministry of Public Health in Cameroon). Prior to enrollment, the study information sheet was provided to each potential participant, and informed consent was then obtained from each participant. Data confidentiality and privacy of participants were ensured by the use of anonymized unique identifiers, and the data were secured in an encrypted passwordprotected computer. Only authorized individuals, such as the principal and co-principal investigators, had access to the survey account. The generated data were used to strengthen the capacity of the target population on better outbreak preparedness and response through result dissemination and exploitation.

Author contributions

ADN: Conceptualization, Data curation, Formal analysis, Writing—review & editing. YB: Data curation, Methodology, Writing—original draft, Writing—review & editing, Validation. JF: Conceptualization, Data curation, Formal analysis, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing. AK: Data curation, Investigation, Software, Writing—original draft. JG: Conceptualization, Formal analysis, Writing—review & editing. NM: Methodology, Project administration, Software, Supervision, Writing—original draft. DM: Formal analysis, Investigation, Methodology, Writing—review & editing. CA: Conceptualization, Formal analysis, Methodology, Visualization, Writing—review & editing. M-LM: Conceptualization, Data curation, Investigation, Visualization, Writing—review & editing. TD: Conceptualization, Data curation, Formal analysis, Writing—review & editing. BT: Data curation, Formal analysis, Project administration, Software, Writing—review & editing. DA: Data curation, Investigation, Resources, Writing—original draft. DN: Investigation, Writing—review & editing. SM: Investigation, Writing—review & editing. EN: Data curation, Formal analysis, Investigation, Writing—review & editing. AT: Investigation, Writing—review & editing. BF: Investigation, Writing—review & editing. IS: Investigation, Writing—review & editing. MT: Investigation, Writing—review & editing. DT: Investigation, Writing—review & editing. WP: Investigation, Writing—review & editing. SS: Funding acquisition, Investigation, Writing—review & editing. ET: Formal analysis, Writing—original draft, Writing—review & editing. LE: Investigation, Writing—review & editing. GE: Investigation, Writing—review & editing. A-CZ-K: Investigation, Writing—review & editing. H-EG: Investigation, Writing—review & editing. NN: Data curation, Methodology, Writing—original draft, Writing—review & editing. VC: Data curation, Writing—original draft, Writing—review & editing. C-FP: Conceptualization, Data curation, Writing—original draft, Writing—review & editing. AN: Writing—original draft, Writing—review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Chantal Biya International Reference Center (CIRCB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

We highly appreciate all HCWs who have contributed to the Mpox online survey.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Questions used to measure the knowledge of monkeypox among general practitioners in Indonesia

Questions used to measure knowledge

No. Question Yes No

1 Monkeypox is prevalent in Southeast Asia countries

2 Monkeypox is prevalent in Western and Central Africa

3 There are many human monkeypox cases in Cameroon (greater than 10 cases)

4 There is an outbreak of human monkeypox in the center region of Cameroon

5 Monkeypox is a viral disease infection

6 Monkeypox is a bacterial disease infection

7 Monkeypox is easily transmitted human-to-human

8 Monkeypox could be transmitted through a bite of an infected monkey

9 Travelers from America continent are the main source of imported cases of monkeypox

10 Monkeypox and smallpox have similar signs and symptoms

11 Monkeypox and smallpox have the same signs and symptoms

12 Flu-like syndrome is one of the early signs or symptoms of human monkeypox

13 Rashes on the skin are one of the signs or symptoms of human monkeypox

14 Papules on the skin are one of the signs or symptoms of human monkeypox

15 Vesicles on the skin are one of the signs or symptoms of human monkeypox

16 Pustules on the skin are one of the signs or symptoms of human monkeypox

17 Lymphadenopathy (swollen lymph nodes) is one clinical sign or symptom that could be used to differentiate monkeypox and smallpox cases

18 One management option for monkeypox patients who are symptomatic is to use paracetamol

19 Antivirals are required in the management of human monkeypox patients

20 Antibiotics are required in the management of human monkeypox patients

21 Diarrhea is one of the signs or symptoms of human monkeypox

Keywords: monkeypox (Mpox), knowledge, healthcare workers, emerging pathogens, Cameroon

Citation: Nka AD, Bouba Y, Fokam J, Ka'e AC, Gabisa JE, Mandeng N, Mfonkou DJT, Ambe CC, Mballa Mpouel M-L, Djikeussi T, Tchounga BK, Ayuk Ngwese DT, Njume D, Mbala Nomo SE, Ngoufack Jagni Semengue E, Tiotsia Tsapi A, Fokou BB, Simo Kamdem IK, Tommo Tchouaket MC, Takou D, Pabo W, Sosso SM, Tandi E, Esso L, Etoundi Mballa GA, Zoung-Kanyi Bissek A-C, Gregory Edie H-E, Ndembi N, Colizzi V, Perno C-F and Ndjolo A (2024) Current knowledge of human Mpox viral infection among healthcare workers in Cameroon calls for capacity-strengthening for pandemic preparedness. Front. Public Health 12:1288139. doi: 10.3389/fpubh.2024.1288139

Received: 03 September 2023; Accepted: 16 February 2024; Published: 12 March 2024.

Reviewed by:

Copyright © 2024 Nka, Bouba, Fokam, Ka'e, Gabisa, Mandeng, Mfonkou, Ambe, Mballa Mpouel, Djikeussi, Tchounga, Ayuk Ngwese, Njume, Mbala Nomo, Ngoufack Jagni Semengue, Tiotsia Tsapi, Fokou, Simo Kamdem, Tommo Tchouaket, Takou, Pabo, Sosso, Tandi, Esso, Etoundi Mballa, Zoung-Kanyi Bissek, Gregory Edie, Ndembi, Colizzi, Perno and Ndjolo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alex Durand Nka, nkaalexdurand@yahoo.com ; Yagai Bouba, romeobouba@yahoo.fr ; Joseph Fokam, josephfokam@gmail.com

† These authors have contributed equally to this work and share first authorship

‡ These authors have contributed equally to this work and share senior authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The next frontier of retirement plan design: 4 big ideas

March 13, 2024

Fiona Greig, Ph.D.

Global Head of Investor Research and Policy, Vanguard Investment Strategy Group

Over the last decade, harnessing the power of defaults has helped improve retirement plan design and participant outcomes. Today, most plans automatically enroll employees, increase their savings rate each year, and invest their balances into age-appropriate portfolios. These features have led to meaningful improvements in participation and savings rates, as well as increased diversification in participant retirement portfolios. 1

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What is the next frontier of plan design? Might we aim to promote financial wellness for workers not just in their current jobs but over the course of their careers? Might we design the next generation of features with an eye toward equity and even short-term needs? We have four big ideas worth exploring.

Smarter default savings rates: Although most people increase their income when switching jobs, they might also decrease their retirement savings rate. For example, a worker could auto-enroll in a company plan at 3% and then increase their savings rate to 8% over five years, but go right back to 3% upon taking a new job with another company. 2 The typical American has nine jobs over the course of their career, resulting in repeated slowdowns in savings and potentially 35% less money saved at retirement. 3 Increasing the default savings rate or nudging new hires to save at their prior rate could go a long way toward maintaining valuable momentum during job changes.

A qualified default investment alternative (QDIA) for individual retirement accounts (IRAs): There is a material cash drag in IRA accounts. 4 Many workers roll their funds into an IRA when they switch jobs, but because IRAs do not have a QDIA, investors must actively choose a portfolio allocation. 5 And, sadly, many do not. As a result, their assets stay in cash for long periods of time, missing out on the potential for significantly higher returns and the power of compounding. Vanguard research shows that 28% of rollovers that entered Vanguard as cash were still in cash seven years later. 6 This cash drag is even greater for younger investors and smaller rollovers. 7 Enabling QDIAs for IRA accounts would allow IRA investors to benefit from the power of good investment defaults just as 401(k) participants have.

“Maximizing” the match for equity, savings, and cost: Currently, the typical employer match formula rewards those who can and do save the most—workers with higher incomes, education, and family wealth. 8 When evaluating match formulas through three criteria of equity, savings, and cost, no single formula stands out in terms of savings, but dollar caps are more equitable and control costs. This presents a potential cost-neutral opportunity to increase equity and savings: Employers might consider using dollar caps to help pay for plan features that do a better job of promoting savings, such as auto-enrollment or a higher default savings rate. Recognizing that employers have different objectives, these criteria can help them evaluate and maximize their existing contributions to meet their plan goals.

Automatic repayment for emergency withdrawals: The SECURE 2.0 Act that became law in 2022 permits $1,000 penalty-free emergency withdrawals with the hope of encouraging more workers to save for retirement and benefit from the employer match. How do we help savers balance their short- and long-term needs? We see that participants who take 401(k) loans continue making regular contributions while repaying their loans. 9 This suggests that participants might be able to “repay” their withdrawals on top of their regular contributions. If plan sponsors nudged participants to automatically repay their withdrawals, workers would be able to access their savings in the short term without compromising their long-term financial wellness.

To close the retirement savings gap, we must continue to innovate. Smarter contribution defaults and an IRA QDIA could help job-switchers maintain their momentum in saving for retirement. Match formulas with dollar caps can help ensure employer contributions go to those who need them most. Emergency withdrawals with automatic repayment could give savers access to liquidity without leakage. These innovations are worth exploring as we push into the next frontier of plan design.

1 Clark, Jeffrey W. and Kevin D. Kukulka (Vanguard 2023). Generational changes in 401(k) behaviors .

2 Indeed, the most common default savings rate is still just 3% ( Vanguard 2023 ).

3 Bureau of Labor Statistics, 2022. Current population survey.

4 There was $12.7 trillion in IRA accounts in 2020, according to IRS SOI Tax Stats Accumulation and Distribution of Individual Retirement Arrangements. In 2021, according to the Employee Benefit Research Institute (EBRI)/Investment Company Institute (ICI) (2021), Frequently Asked Questions About 401(k) Plan Research , there was $6.7 trillion in 401(k) dollars.

5 According to the IRS, in 2020, 88% of the $701 billion in total IRA inflows came from rollovers, with the remaining 12% coming from direct contributions.

6 Much of the $600-plus billion flowing to IRAs each year through rollovers enters the IRAs as cash. A primary driver of rollover cash is transfers between financial institutions: In cases where the IRA provider is different than the 401(k) recordkeeper, the 401(k) assets must generally be liquidated and moved as cash. According to a research report conducted by Hearts & Wallets in 2022, about 40% of rollovers involve transfers between institutions, so the share of rollovers moving as cash is likely at least as large.

7 Our findings align with research from other industry sources. For example, an ICI study (Holden, Sarah and Steven Bass (2018). The IRA Investor Profile: Traditional IRA Investors’ Activity, 2007–2016 ) showed that among rollovers between $1,000 and $5,000 in 2008, over 40% were entirely invested in money market funds seven years later. EBRI also documents a large cash allocation post rollover, especially for low-balance investors ( Comparing Asset Allocation Before and After a Rollover From 401(k) Plans to Individual Retirement Accounts , 2019).

8 National evidence is documented in Choukhmane et al., (2023). Who Benefits from Retirement Saving Incentives in the U.S.? Analysis of 1,365 plan records kept by Vanguard between 2013 and 2022 shows that top earners receive 43% of W-2 income and 39% percent of benefit income, compared to 44% of employer contributions.

9 Beshears et al. (2024). Does 401(k) loan repayment crowd out retirement saving? Evidence from administrative data and implications for plan design .

Related links:

  • The Vanguard Retirement Outlook: A national perspective on retirement readiness (31-page PDF, issued July 2023)
  • Retirement readiness: Seeking solutions that benefit all Americans (commentary, issued October 2023)
  • The state of retirement in five figures (infographic, issued October 2023)

All investing is subject to risk, including the possible loss of the money you invest. There is no guarantee that any particular asset allocation or mix of funds will meet your investment objectives or provide you with a given level of income. Diversification does not ensure a profit or protect against a loss.

There are important factors to consider when rolling over assets to an IRA or an employer retirement plan account or leaving assets in an employer retirement plan account. These factors include, but are not limited to, investment options in each type of account, fees and expenses, available services, potential withdrawal penalties, protection from creditors and legal judgments, required minimum distributions, and tax consequences of rolling over employer stock to an IRA.

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  • v.45(1); Jan-Feb 2010

Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand.

To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs.

Description:

The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style . At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary.

Advantages:

Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.

Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.

A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.

Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).

The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.

WHAT IS A STUDY (OR EXPERIMENTAL OR RESEARCH) DESIGN?

The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.

A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.

Table. Elements of a “Methods” Section

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The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.

The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.

Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.

The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.

The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.

STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS

Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:

The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6

Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)

The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.

Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.

For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.

MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS

With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.

As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.

CONCLUSIONS

The goal of scientific writing, or any writing, for that matter, is to communicate information. Including 2 design statements or subsections in scientific papers—one to explain how the data were collected and another to explain how they were statistically analyzed—will improve the clarity of communication and bring praise from readers. To summarize:

  • Purge from your thoughts and vocabulary the idea that experimental design and statistical design are synonymous.
  • Study or experimental design plays a much broader role than simply defining and directing the statistical analysis of an experiment.
  • A properly written study design serves as a road map to the “Methods” section of an experiment and, therefore, improves communication with the reader.
  • Study design should include a description of the type of design used, each factor (and each level) involved in the experiment, and the time at which each measurement was made.
  • Clarify when the variables involved in data collection and data analysis are different, such as when data analysis involves only a subset of a collected variable or a resultant variable from the mathematical manipulation of 2 or more collected variables.

Acknowledgments

Thanks to Thomas A. Cappaert, PhD, ATC, CSCS, CSE, for suggesting the link between R.A. Fisher and the melding of the concepts of research design and statistics.

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Not Just for Scooby-Doo Anymore — the Secret Door Is Having a Moment

Homeowners are adding hidden doors and rooms to foil burglars, eke out extra storage space and prepare for Armageddon.

Bookcase-fronted doors leading to hidden stairs at the Morgan Library & Museum in New York City. The doors were designed to blend in with the shelves around them; slender brass handles are the only giveaway. Credit... By Lanna Apisukh

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By Jane Margolies

  • March 7, 2024

This article is part of our Design special section about innovative surfaces in architecture, interiors and products.

Tabitha Kane is a co-host of a true crime podcast so it might not come as a complete surprise to learn that when she and her husband were planning a new house for their family in Dallas, she cooked up the idea of adding a secret room.

At first, the couple thought they’d get their contractor to make the door to it look like a wall cabinet. But then they found an Arizona firm named Creative Home Engineering that rigged up a faux fireplace for Mrs. Kane’s home office that rotates to provide entry to the room when a member of the family places a hand on a biometric touch pad that recognizes their fingerprints.

Tabitha Kane sits in a black room. Behind her, a faux fireplace is swung partly open, revealing another space behind.

“It makes the house more fun,” Mrs. Kane said.

Armchair sleuths aren’t the only devotees of the open-sesame game these days. Hidden doors and secret rooms have become an increasingly popular feature in American homes, whether the goal is foiling burglars, eking out extra storage or creating so-called safe, or panic, rooms for doomsday scenarios.

And then there are the D.I.Y.ers who just like geeking out on things like pivot hinges. Or those who are still in thrall of beloved childhood books and shows, like “The Lion, the Witch and the Wardrobe,” with its portal to Narnia, or Scooby-Doo cartoons , in which characters are always dropping through trap doors or stumbling on secret passageways. And that’s not to mention those who are enamored of the mechanical marvels of Indiana Jones or James Bond movies.

No one appears to keep track of how many such sleights of hand are cropping up in American homes. But evidence of interest abounds: Houzz, a website that connects homeowners with design and remodeling professionals, reported that searches on their site for the terms “trap doors,” “kitchens with hidden pantries” and “speakeasy home bar lounge” had all more than doubled between 2022 and 2023. The subject has inspired all manner of blog posts , subreddits and Pinterest boards . On TikTok, posts on the “Hidden Room” account have garnered some 165,000 likes.

Companies that make pre-hung, ready-to-install doors that masquerade as bookcases and pool cue racks say that business started booming at the height of the pandemic in 2020, when Americans holed up at home dove into renovation projects. Some homeowners who turned bedrooms into offices for remote work swapped out regular closet doors for ones that double as shelving units to make the spaces more functional, as well as more professional-looking on Zoom calls.

Hide-A-Way Doors , a Tennessee company whose standard products start at around $1,250, had between 77 and 135 orders a month in 2023, said Christopher P. Rupell, Sr., the company’s chief executive. And Home Depot added goods from a business named Murphy Door to its website in 2021 and introduced them to some stores last year to keep up with the “rising trend,” said Madison Stevens, a representative for the company.

Part of the fun is in the trigger that opens the door, which can take practically any form, from a book to a beer bottle. One company sold out its bronze-hued Shakespeare bust with a head that tilts back to reveal a switch — presumably to fans of the campy 1960s Batman television show who may have fondly recalled the Caped Crusader using such a device to gain access to the Batpoles descending to the Batcave.

Architects and designers who work on high-end homes may turn up their noses at such gags, or at the synthetic board, MDF, used in some of the manufactured products. But they’re not above playing hide-and-seek, too, especially when creating a seamless-looking space.

The architecture firm MKCA recently added a jib door — one that’s mounted flush with the wall, often without a frame or visible hinges — for a powder room on the parlor floor of a gut-renovated brownstone in Brooklyn. The firm had the door painted the same pale blue as the wall and added a baseboard molding so it all but blends in.

“I can think of few instances where I’ve said the words ‘hidden door’ and clients said, ‘no, thank you,’” Michael K. Chen, the firm’s principal, recalled. “Who doesn’t want a secret door in their home?”

The contrivances have a long, varied history.

The ancient Egyptians used hidden doors in pyramids to thwart thieves who might be after the riches intended to accompany deceased Pharaohs into the afterlife. Centuries later, secret passages in medieval European castles were designed to allow occupants to survive a siege.

articles about research design

At the turn of the 20th century, the financier J. Pierpont Morgan had the architect Charles McKim design a jewel-box library next to his house in New York — now the Morgan Library & Museum — with bookcase doors in walnut and fruitwood that maximize shelf space while providing staff easy access to spiral stairs leading to the second and third tiers of the repository. (Slender brass handles are the only giveaway.)

In another room on the same floor, movable bookcases in Mr. Morgan’s own study likely hid his stash of “naughty” volumes, said Jennifer Tonkovich, a Morgan curator. On a recent morning Ms. Tonkovich gently pushed back one of the bookcases, then slid in front of it an adjacent bookcase that smoothly rolled across on a brass rail, revealing a previously concealed opening for books that, she noted, “maybe a gentleman wouldn’t want everyone to see.”

Recently, some companies have been creating speakeasy-style rooms harking back to Prohibition days to add interest to workplaces . But secret rooms are much more common in homes, according to companies that make and sell doors for them.

Lee Spangenberg said that he got into making doors that double as shelves and cabinets after he and his wife tried to shoehorn a crib, a changing table and an array of toys into their son’s room when he was a baby. The closet door, Mr. Spangenberg decided, could be working harder.

At first, he thought he would call his company Space Utilization Doors, but then he switched to Secret Doorways to emphasize mystery rather than functionality. He makes and installs his handiwork in homes within a reasonable driving distance of his workshop in central Ohio and sells hardware kits ($119-229) and plans to contractors and hobbyists farther afield.

“Ten years ago everyone wanted to put barn doors into their house,” Mr. Spangenberg said. “Now a lot more people are doing this type of thing.”

Some homeowners use secret rooms to hide guns or safes, or to provide a place to shelter during a hurricane or even, perhaps, Armageddon — though likely none are as extravagant as the 5,000-square-foot underground bunker Mark Zuckerberg is reportedly building in Hawaii.

Steve Humble, the founder of Creative Home Engineering, noted that his company makes “high security” doors with steel cores — which, he said, are “bulletproof” and “built like a bank vault.”

Julie Petty, who lives outside Denver, didn’t have anything to hide, unless it was the detritus of daily life with two teenage sons and two dogs. She and her husband bought their 1980s house in 2019 and hired Laura Medicus , an interior designer, to help them remodel it.

The Pettys wanted built-in storage in the dining area, which had two doors on the same short wall — a windowed one leading outside to the yard, and a solid one leading to a finished basement space that functions as a den, study and sometime guest room.

The door leading downstairs “was not in a comfortable place,” Ms. Medicus said.

The Pettys purchased three bookcases from a local cabinet shop, and worked with a trim carpenter who figured out how to turn one of them into a door. He also rose to the challenge of installing the new-to-him hardware , including a pivot hinge that mounted to the top of the door opening as well as a bottom pivot that was bolted to the floor. Now that the door is installed, the Pettys make sure to put only lightweight items on it so it opens and closes easily.

Other designers have disguised doors to home bars.

Tammy Connor , an interior designer, and D. Stanley Dixon , an architect, added a tiny bar to the library of a 1930s home in Atlanta, tucking it behind a narrow door faced with the room’s blue-painted paneling. And in a New York penthouse apartment, Yellow House Architects and the decorating firm Redd Kaihoi slipped a bar into the arched, paneled section of a hallway off the living room. When the bar’s pocket door is closed, the space is hidden from sight; opening the door and sliding it into a wall slot leaves the hall clear.

For some homeowners, installing one trick door leads to the idea for another.

Matthew and Tara Duhan, board game enthusiasts with two sons, paid Hide-A-Way, the Tennessee company that makes pre-hung doors, about $10,000 to build a black-stained hickory wall unit for the basement of their house in a Chicago suburb, requesting cubbies deep enough to fit their chunky cardboard board game boxes. One section of cubbies is actually a door, and when one of the Duhans presses on a certain box, it clicks the button on a key fob inside and — voilà! — the door opens to their wine cellar.

The Duhans then ordered a white-painted door with built-in shelves and a cabinet (about $1,250), to solve a problem in their foyer. There, opposite the front door, had been what appeared to be the door to a coat closet — which meant that visitors were always opening it looking for a hanger only to find the house’s HVAC unit. Now the shelves hold framed family photos, and visitors have no clue what’s behind.

“I almost forget it’s a door myself,” Mrs. Duhan said.

As for the true crime podcaster and her husband, they didn’t stop at their revolving fireplace, which leads to a tornado shelter, and cost around $30,000. They also ordered hardware for three more hidden doors and had their contractor build them, then install them in their children’s bedrooms.

In their daughter’s room, a door that looks like an ordinary wall mirror opens to a hangout area for her and her friends. An athletic son’s mirrored door leads to his trophy room, and a son who’s a YouTuber has a section of a paneled wall that opens to a studio where he makes videos.

“When they show their friends the house, this is what they show first,” Mrs. Kane said.

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    Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies. Frequently asked questions. Introduction. Step 1. Step 2.

  8. Clinical research study designs: The essentials

    Introduction. In clinical research, our aim is to design a study, which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods that can be translated to the "real world" setting. 1 Before choosing a study design, one must establish aims and objectives of the study, and choose an appropriate target population that is most representative of ...

  9. Research design: qualitative, quantitative, and mixed methods

    This review examines John W. Creswell and David Creswell's sixth edition, which covers the most popular research methods, offering readers a comprehensive understanding and practical guidance in qualitative, quantitative, and mixed methods. The review includes observations on existing drawbacks, gaps, and ideas on potential areas for improvement in the book. The book is an excellent entry ...

  10. What is Research Design? Types, Elements and Examples

    The research design categories under this are descriptive, experimental, correlational, diagnostic, and explanatory. Data analysis involves interpretation and narrative analysis. Data analysis involves statistical analysis and hypothesis testing. The reasoning used to synthesize data is inductive.

  11. Full article: Design-based research: What it is and why it matters to

    Conclusion. Design-based research methods are a thirty-year old tradition from the learning sciences that have been taken up in many domains as a way to study designed interventions that challenge the traditional relationship between research and design, as is the case with online learning.

  12. A Phenomenological Research Design Illustrated

    Abstract. This article distills the core principles of a phenomenological research design and, by means of a specific study, illustrates the phenomenological methodology. After a brief overview of the developments of phenomenology, the research paradigm of the specific study follows. Thereafter the location of the data, the data-gathering the ...

  13. What Is Research Design? 8 Types + Examples

    Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data. Research designs for quantitative studies include descriptive, correlational, experimental and quasi-experimenta l designs. Research designs for qualitative studies include phenomenological ...

  14. Experimental Research Design

    Experimental research design is centrally concerned with constructing research that is high in causal (internal) validity. Randomized experimental designs provide the highest levels of causal validity. Quasi-experimental designs have a number of potential threats to their causal validity. Yet, new quasi-experimental designs adopted from fields ...

  15. What is a Research Design? Definition, Types, Methods and Examples

    Research design methods refer to the systematic approaches and techniques used to plan, structure, and conduct a research study. The choice of research design method depends on the research questions, objectives, and the nature of the study. Here are some key research design methods commonly used in various fields: 1.

  16. (PDF) Research Design

    Research design is the plan, structure and strategy and investigation concaved so as to obtain search question and control variance" (Borwankar, 1995). Henry Manheim says that research design not ...

  17. Understanding Research Study Designs

    Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23 (Suppl 4):S305-S307. Keywords: Clinical trials as topic, Observational studies as topic, Research designs. We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.

  18. Full article: Applied research by design: an experimental collaborative

    The research question and method. The research question is: To which extent is it possible to simulate in a multi-disciplinary design and planning studio the context, conditions and principles for the preparation for a spatial / general / master / comprehensive plan that addresses sustainability in the built environment with a holistic, multi-factor and multi-scalar approach?

  19. The use of co-production, co-design and co-creation to mobilise

    Knowledge mobilisation is a term used in healthcare research to describe the process of generating, sharing and using evidence. 'Co'approaches, such as co-production, co-design and co-creation, have been proposed as a way of overcoming the knowledge to practice gap. There is a need to understand why researchers choose to adopt these approaches, how they achieve knowledge mobilisation in ...

  20. Single dose of LSD provides immediate, lasting anxiety relief, study

    In 1968, the United States outlawed LSD and research projects were shut down or forced underground. Then came the 1970 Controlled Substances Act, signed by President Richard Nixon.

  21. Onur Avci recognized by American Institute of Steel Construction for

    The AISC's Need for Speed initiative aims to increase design and fabrication speed in the construction of steel buildings and bridges. Avci and his team (including faculty from WVU, Northeastern, Johns Hopkins, Virginia Tech, and Iowa State University) have developed an all-steel modular system design that eliminates the need to cast concrete ...

  22. Recent progress and challenges in silicon-based anode materials for

    In this detailed review, we highlight the key issues, current advances, and prospects in the rational design of Si-based electrodes for practical applications. We first explain the fundamental electrochemistry of Si and the importance of Si-based anodes in LIBs. ... Finally, we highlight that research and development on Si-based anodes are ...

  23. Generative AI fuels creative physical product design but is no magic

    In product research and design alone, McKinsey estimates gen AI could unlock $60 billion in productivity. 1. High-fidelity concept images of modern welding helmets powered by the Internet of Things that were created using a generative AI text-to-image software. Through iterative prompting, the industrial designer refined the initial designs to ...

  24. Research Design Considerations

    Purposive sampling is often used in qualitative research, with a goal of finding information-rich cases, not to generalize. 6. Be reflexive: Examine the ways in which your history, education, experiences, and worldviews have affected the research questions you have selected and your data collection methods, analyses, and writing. 13. Go to:

  25. Frontiers

    This article is part of the Research Topic Emerging and Re-emerging Viral Infections: Epidemiology, Pathogenesis and New Methods for Control and Prevention View all 23 articles. ... 2.1 Study design and settings. Within the framework of the country's response to Mpox, a cross-sectional online survey was conducted from August to October 2022 to ...

  26. EU regulators and industry clash on pharmaceutical reform package

    BRUSSELS @ European Union officials said a proposal to reform the EU pharmaceutical legislation will go a long way towards providing equal access to new medicines, yet the proposal faced pushback from representatives of pharmaceutical companies who argued that shortening the marketing exclusivity periods for new drugs will hurt innovation and stymie clinical research in the region.

  27. The next frontier of retirement plan design: 4 big ideas

    The next frontier of retirement plan design: 4 big ideas. March 13, 2024. Fiona Greig, Ph.D. Global Head of Investor Research and Policy, Vanguard Investment Strategy Group. Over the last decade, harnessing the power of defaults has helped improve retirement plan design and participant outcomes. Today, most plans automatically enroll employees ...

  28. Study/Experimental/Research Design: Much More Than Statistics

    Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping ...

  29. Secret Doors Are Having a Major Moment

    On TikTok, posts on the "Hidden Room" account have garnered some 165,000 likes. Mrs. Kane and her husband decided to incorporate more hidden doors into their Dallas home, including one in a ...