dissertation on stress

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Stress and Coping Mechanisms Among College Students

• Masters Thesis
• Cornejo, Joaquin
• Park, Hyun Sun
• Brown, Jodi
• Acuña, Maria
• Social Work
• California State University, Northridge
• self-acceptance.
• college students
• self-compassion
• Dissertations, Academic -- CSUN -- Social Work.
• coping mechanism
• 2020-06-01T19:29:25Z
• http://hdl.handle.net/10211.3/216140
• by Joaquin Cornejo
• California State University, Northridge. Department of Social Work.

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

• Open access
• Published: 17 April 2024

Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study

• Rawhia Salah Dogham 1 ,
• Heba Fakieh Mansy Ali 1 ,
• Asmaa Saber Ghaly 3 ,
• Nermine M. Elcokany 2 ,
• Mohamed Mahmoud Seweid 4 &
• Ayman Mohamed El-Ashry   ORCID: orcid.org/0000-0001-7718-4942 5  

BMC Nursing volume  23 , Article number:  249 ( 2024 ) Cite this article

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Metrics details

Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being.

This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students.

Design and Method

A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2 F).

Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches.

Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being.

Nursing implication

Understanding academic stress’s impact on nursing students’ learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.

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Introduction

Nursing education is a demanding field that requires students to acquire extensive knowledge and skills to provide competent and compassionate care. Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [ 1 , 2 ]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information. Surface learning approaches focus on memorization and reproducing information without a more profound understanding. Strategic learning approaches aim to achieve high grades by adopting specific strategies, such as memorization techniques or time management skills [ 3 , 4 , 5 ].

Nursing education stands out due to its focus on practical training, where the blend of academic and clinical coursework becomes a significant stressor for students, despite academic stress being shared among all university students [ 6 , 7 , 8 ]. Consequently, nursing students are recognized as prone to high-stress levels. Stress is the physiological and psychological response that occurs when a biological control system identifies a deviation between the desired (target) state and the actual state of a fitness-critical variable, whether that discrepancy arises internally or externally to the human [ 9 ]. Stress levels can vary from objective threats to subjective appraisals, making it a highly personalized response to circumstances. Failure to manage these demands leads to stress imbalance [ 10 ].

Nursing students face three primary stressors during their education: academic, clinical, and personal/social stress. Academic stress is caused by the fear of failure in exams, assessments, and training, as well as workload concerns [ 11 ]. Clinical stress, on the other hand, arises from work-related difficulties such as coping with death, fear of failure, and interpersonal dynamics within the organization. Personal and social stressors are caused by an imbalance between home and school, financial hardships, and other factors. Throughout their education, nursing students have to deal with heavy workloads, time constraints, clinical placements, and high academic expectations. Multiple studies have shown that nursing students experience higher stress levels compared to students in other fields [ 12 , 13 , 14 ].

Research has examined the relationship between academic stress and coping strategies among nursing students, but no studies focus specifically on the learning approach and academic stress. However, existing literature suggests that students interested in nursing tend to experience lower levels of academic stress [ 7 ]. Therefore, interest in nursing can lead to deep learning approaches, which promote a comprehensive understanding of the subject matter, allowing students to feel more confident and less overwhelmed by coursework and exams. Conversely, students employing surface learning approaches may experience higher stress levels due to the reliance on memorization [ 3 ].

Understanding the interplay between academic stress and learning approaches among nursing students is essential for designing effective educational interventions. Nursing educators can foster deep learning approaches by incorporating active learning strategies, critical thinking exercises, and reflection activities into the curriculum [ 15 ]. Creating supportive learning environments encouraging collaboration, self-care, and stress management techniques can help alleviate academic stress. Additionally, providing mentorship and counselling services tailored to nursing students’ unique challenges can contribute to their overall well-being and academic success [ 16 , 17 , 18 ].

Despite the scarcity of research focusing on the link between academic stress and learning methods in nursing students, it’s crucial to identify the unique stressors they encounter. The intensity of these stressors can be connected to the learning strategies employed by these students. Academic stress and learning approach are intertwined aspects of the student experience. While academic stress can influence learning approaches, the choice of learning approach can also impact the level of academic stress experienced. By understanding this relationship and implementing strategies to promote healthy learning approaches and manage academic stress, educators and institutions can foster an environment conducive to deep learning and student well-being.

Hence, this study aims to investigate the correlation between academic stress and learning approaches experienced by nursing students.

Study objectives

Assess the levels of academic stress among nursing students.

Assess the learning approaches among nursing students.

Identify the relationship between academic stress and learning approach among nursing students.

Identify the effect of academic stress and related factors on learning approach and among nursing students.

Materials and methods

Research design.

A cross-sectional descriptive correlation research design adhering to the STROBE guidelines was used for this study.

A research project was conducted at Alexandria Nursing College, situated in Egypt. The college adheres to the national standards for nursing education and functions under the jurisdiction of the Egyptian Ministry of Higher Education. Alexandria Nursing College comprises nine specialized nursing departments that offer various nursing specializations. These departments include Nursing Administration, Community Health Nursing, Gerontological Nursing, Medical-Surgical Nursing, Critical Care Nursing, Pediatric Nursing, Obstetric and Gynecological Nursing, Nursing Education, and Psychiatric Nursing and Mental Health. The credit hour system is the fundamental basis of both undergraduate and graduate programs. This framework guarantees a thorough evaluation of academic outcomes by providing an organized structure for tracking academic progress and conducting analyses.

Participants and sample size calculation

The researchers used the Epi Info 7 program to calculate the sample size. The calculations were based on specific parameters such as a population size of 9886 students for the academic year 2022–2023, an expected frequency of 50%, a maximum margin of error of 5%, and a confidence coefficient of 99.9%. Based on these parameters, the program indicated that a minimum sample size of 976 students was required. As a result, the researchers recruited a convenient sample of 1010 nursing students from different academic levels during the 2022–2023 academic year [ 19 ]. This sample size was larger than the minimum required, which could help to increase the accuracy and reliability of the study results. Participation in the study required enrollment in a nursing program and voluntary agreement to take part. The exclusion criteria included individuals with mental illnesses based on their response and those who failed to complete the questionnaires.

socio-demographic data that include students’ age, sex, educational level, hours of sleep at night, hours spent studying, and GPA from the previous semester.

Tool two: the perceived stress scale (PSS)

It was initially created by Sheu et al. (1997) to gauge the level and nature of stress perceived by nursing students attending Taiwanese universities [ 20 ]. It comprises 29 items rated on a 5-point Likert scale, where (0 = never, 1 = rarely, 2 = sometimes, 3 = reasonably often, and 4 = very often), with a total score ranging from 0 to 116. The cut-off points of levels of perceived stress scale according to score percentage were low < 33.33%, moderate 33.33–66.66%, and high more than 66.66%. Higher scores indicate higher stress levels. The items are categorized into six subscales reflecting different sources of stress. The first subscale assesses “stress stemming from lack of professional knowledge and skills” and includes 3 items. The second subscale evaluates “stress from caring for patients” with 8 items. The third subscale measures “stress from assignments and workload” with 5 items. The fourth subscale focuses on “stress from interactions with teachers and nursing staff” with 6 items. The fifth subscale gauges “stress from the clinical environment” with 3 items. The sixth subscale addresses “stress from peers and daily life” with 4 items. El-Ashry et al. (2022) reported an excellent internal consistency reliability of 0.83 [ 21 ]. Two bilingual translators translated the English version of the scale into Arabic and then back-translated it into English by two other independent translators to verify its accuracy. The suitability of the translated version was confirmed through a confirmatory factor analysis (CFA), which yielded goodness-of-fit indices such as a comparative fit index (CFI) of 0.712, a Tucker-Lewis index (TLI) of 0.812, and a root mean square error of approximation (RMSEA) of 0.100.

Tool three: revised study process questionnaire (R-SPQ-2 F)

It was developed by Biggs et al. (2001). It examines deep and surface learning approaches using only 20 questions; each subscale contains 10 questions [ 22 ]. On a 5-point Likert scale ranging from 0 (never or only rarely true of me) to 4 (always or almost always accurate of me). The total score ranged from 0 to 80, with a higher score reflecting more deep or surface learning approaches. The cut-off points of levels of revised study process questionnaire according to score percentage were low < 33%, moderate 33–66%, and high more than 66%. Biggs et al. (2001) found that Cronbach alpha value was 0.73 for deep learning approach and 0.64 for the surface learning approach, which was considered acceptable. Two translators fluent in English and Arabic initially translated a scale from English to Arabic. To ensure the accuracy of the translation, they translated it back into English. The translated version’s appropriateness was evaluated using a confirmatory factor analysis (CFA). The CFA produced several goodness-of-fit indices, including a Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100. Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100.

Ethical considerations

The Alexandria University College of Nursing’s Research Ethics Committee provided ethical permission before the study’s implementation. Furthermore, pertinent authorities acquired ethical approval at participating nursing institutions. The vice deans of the participating institutions provided written informed consent attesting to institutional support and authority. By giving written informed consent, participants confirmed they were taking part voluntarily. Strict protocols were followed to protect participants’ privacy during the whole investigation. The obtained personal data was kept private and available only to the study team. Ensuring participants’ privacy and anonymity was of utmost importance.

Tools validity

The researchers created tool one after reviewing pertinent literature. Two bilingual translators independently translated the English version into Arabic to evaluate the applicability of the academic stress and learning approach tools for Arabic-speaking populations. To assure accuracy, two additional impartial translators back-translated the translation into English. They were also assessed by a five-person jury of professionals from the education and psychiatric nursing departments. The scales were found to have sufficiently evaluated the intended structures by the jury.

Pilot study

A preliminary investigation involved 100 nursing student applicants, distinct from the final sample, to gauge the efficacy, clarity, and potential obstacles in utilizing the research instruments. The pilot findings indicated that the instruments were accurate, comprehensible, and suitable for the target demographic. Additionally, Cronbach’s Alpha was utilized to further assess the instruments’ reliability, demonstrating internal solid consistency for both the learning approaches and academic stress tools, with values of 0.91 and 0.85, respectively.

Data collection

The researchers convened with each qualified student in a relaxed, unoccupied classroom in their respective college settings. Following a briefing on the study’s objectives, the students filled out the datasheet. The interviews typically lasted 15 to 20 min.

Data analysis

The data collected were analyzed using IBM SPSS software version 26.0. Following data entry, a thorough examination and verification were undertaken to ensure accuracy. The normality of quantitative data distributions was assessed using Kolmogorov-Smirnov tests. Cronbach’s Alpha was employed to evaluate the reliability and internal consistency of the study instruments. Descriptive statistics, including means (M), standard deviations (SD), and frequencies/percentages, were computed to summarize academic stress and learning approaches for categorical data. Student’s t-tests compared scores between two groups for normally distributed variables, while One-way ANOVA compared scores across more than two categories of a categorical variable. Pearson’s correlation coefficient determined the strength and direction of associations between customarily distributed quantitative variables. Hierarchical regression analysis identified the primary independent factors influencing learning approaches. Statistical significance was determined at the 5% (p < 0.05).

Table  1 presents socio-demographic data for a group of 1010 nursing students. The age distribution shows that 38.8% of the students were between 18 and 21 years old, 32.9% were between 21 and 24 years old, and 28.3% were between 24 and 28 years old, with an average age of approximately 22.79. Regarding gender, most of the students were female (77%), while 23% were male. The students were distributed across different educational years, a majority of 34.4% in the second year, followed by 29.4% in the fourth year. The students’ hours spent studying were found to be approximately two-thirds (67%) of the students who studied between 3 and 6 h. Similarly, sleep patterns differ among the students; more than three-quarters (77.3%) of students sleep between 5- to more than 7 h, and only 2.4% sleep less than 2 h per night. Finally, the student’s Grade Point Average (GPA) from the previous semester was also provided. 21% of the students had a GPA between 2 and 2.5, 40.9% had a GPA between 2.5 and 3, and 38.1% had a GPA between 3 and 3.5.

Figure  1 provides the learning approach level among nursing students. In terms of learning approach, most students (55.0%) exhibited a moderate level of deep learning approach, followed by 25.9% with a high level and 19.1% with a low level. The surface learning approach was more prevalent, with 47.8% of students showing a moderate level, 41.7% showing a low level, and only 10.5% exhibiting a high level.

Nursing students? levels of learning approach (N=1010)

Figure  2 provides the types of academic stress levels among nursing students. Among nursing students, various stressors significantly impact their academic experiences. Foremost among these stressors are the pressure and demands associated with academic assignments and workload, with 30.8% of students attributing their high stress levels to these factors. Challenges within the clinical environment are closely behind, contributing significantly to high stress levels among 25.7% of nursing students. Interactions with peers and daily life stressors also weigh heavily on students, ranking third among sources of high stress, with 21.5% of students citing this as a significant factor. Similarly, interaction with teachers and nursing staff closely follow, contributing to high-stress levels for 20.3% of nursing students. While still significant, stress from taking care of patients ranks slightly lower, with 16.7% of students reporting it as a significant factor contributing to their academic stress. At the lowest end of the ranking, but still notable, is stress from a perceived lack of professional knowledge and skills, with 15.9% of students experiencing high stress in this area.

Nursing students? levels of academic stress subtypes (N=1010)

Figure  3 provides the total levels of academic stress among nursing students. The majority of students experienced moderate academic stress (56.3%), followed by those experiencing low academic stress (29.9%), and a minority experienced high academic stress (13.8%).

Nursing students? levels of total academic stress (N=1010)

Table  2 displays the correlation between academic stress subscales and deep and surface learning approaches among 1010 nursing students. All stress subscales exhibited a negative correlation regarding the deep learning approach, indicating that the inclination toward deep learning decreases with increasing stress levels. The most significant negative correlation was observed with stress stemming from the lack of professional knowledge and skills (r=-0.392, p < 0.001), followed by stress from the clinical environment (r=-0.109, p = 0.001), stress from assignments and workload (r=-0.103, p = 0.001), stress from peers and daily life (r=-0.095, p = 0.002), and stress from patient care responsibilities (r=-0.093, p = 0.003). The weakest negative correlation was found with stress from interactions with teachers and nursing staff (r=-0.083, p = 0.009). Conversely, concerning the surface learning approach, all stress subscales displayed a positive correlation, indicating that heightened stress levels corresponded with an increased tendency toward superficial learning. The most substantial positive correlation was observed with stress related to the lack of professional knowledge and skills (r = 0.365, p < 0.001), followed by stress from patient care responsibilities (r = 0.334, p < 0.001), overall stress (r = 0.355, p < 0.001), stress from interactions with teachers and nursing staff (r = 0.262, p < 0.001), stress from assignments and workload (r = 0.262, p < 0.001), and stress from the clinical environment (r = 0.254, p < 0.001). The weakest positive correlation was noted with stress stemming from peers and daily life (r = 0.186, p < 0.001).

Table  3 outlines the association between the socio-demographic characteristics of nursing students and their deep and surface learning approaches. Concerning age, statistically significant differences were observed in deep and surface learning approaches (F = 3.661, p = 0.003 and F = 7.983, p < 0.001, respectively). Gender also demonstrated significant differences in deep and surface learning approaches (t = 3.290, p = 0.001 and t = 8.638, p < 0.001, respectively). Female students exhibited higher scores in the deep learning approach (31.59 ± 8.28) compared to male students (29.59 ± 7.73), while male students had higher scores in the surface learning approach (29.97 ± 7.36) compared to female students (24.90 ± 7.97). Educational level exhibited statistically significant differences in deep and surface learning approaches (F = 5.599, p = 0.001 and F = 17.284, p < 0.001, respectively). Both deep and surface learning approach scores increased with higher educational levels. The duration of study hours demonstrated significant differences only in the surface learning approach (F = 3.550, p = 0.014), with scores increasing as study hours increased. However, no significant difference was observed in the deep learning approach (F = 0.861, p = 0.461). Hours of sleep per night and GPA from the previous semester did not exhibit statistically significant differences in deep or surface learning approaches.

Table  4 presents a multivariate linear regression analysis examining the factors influencing the learning approach among 1110 nursing students. The deep learning approach was positively influenced by age, gender (being female), educational year level, and stress from teachers and nursing staff, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by stress from a lack of professional knowledge and skills. The other factors do not significantly influence the deep learning approach. On the other hand, the surface learning approach was positively influenced by gender (being female), educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by gender (being male). The other factors do not significantly influence the surface learning approach. The adjusted R-squared values indicated that the variables in the model explain 17.8% of the variance in the deep learning approach and 25.5% in the surface learning approach. Both models were statistically significant (p < 0.001).

Nursing students’ academic stress and learning approaches are essential to planning for effective and efficient learning. Nursing education also aims to develop knowledgeable and competent students with problem-solving and critical-thinking skills.

The study’s findings highlight the significant presence of stress among nursing students, with a majority experiencing moderate to severe levels of academic stress. This aligns with previous research indicating that academic stress is prevalent among nursing students. For instance, Zheng et al. (2022) observed moderated stress levels in nursing students during clinical placements [ 23 ], while El-Ashry et al. (2022) found that nearly all first-year nursing students in Egypt experienced severe academic stress [ 21 ]. Conversely, Ali and El-Sherbini (2018) reported that over three-quarters of nursing students faced high academic stress. The complexity of the nursing program likely contributes to these stress levels [ 24 ].

The current study revealed that nursing students identified the highest sources of academic stress as workload from assignments and the stress of caring for patients. This aligns with Banu et al.‘s (2015) findings, where academic demands, assignments, examinations, high workload, and combining clinical work with patient interaction were cited as everyday stressors [ 25 ]. Additionally, Anaman-Torgbor et al. (2021) identified lectures, assignments, and examinations as predictors of academic stress through logistic regression analysis. These stressors may stem from nursing programs emphasizing the development of highly qualified graduates who acquire knowledge, values, and skills through classroom and clinical experiences [ 26 ].

The results regarding learning approaches indicate that most nursing students predominantly employed the deep learning approach. Despite acknowledging a surface learning approach among the participants in the present study, the prevalence of deep learning was higher. This inclination toward the deep learning approach is anticipated in nursing students due to their engagement with advanced courses, requiring retention, integration, and transfer of information at elevated levels. The deep learning approach correlates with a gratifying learning experience and contributes to higher academic achievements [ 3 ]. Moreover, the nursing program’s emphasis on active learning strategies fosters critical thinking, problem-solving, and decision-making skills. These findings align with Mahmoud et al.‘s (2019) study, reporting a significant presence (83.31%) of the deep learning approach among undergraduate nursing students at King Khalid University’s Faculty of Nursing [ 27 ]. Additionally, Mohamed &Morsi (2019) found that most nursing students at Benha University’s Faculty of Nursing embraced the deep learning approach (65.4%) compared to the surface learning approach [ 28 ].

The study observed a negative correlation between the deep learning approach and the overall mean stress score, contrasting with a positive correlation between surface learning approaches and overall stress levels. Elevated academic stress levels may diminish motivation and engagement in the learning process, potentially leading students to feel overwhelmed, disinterested, or burned out, prompting a shift toward a surface learning approach. This finding resonates with previous research indicating that nursing students who actively seek positive academic support strategies during academic stress have better prospects for success than those who do not [ 29 ]. Nebhinani et al. (2020) identified interface concerns and academic workload as significant stress-related factors. Notably, only an interest in nursing demonstrated a significant association with stress levels, with participants interested in nursing primarily employing adaptive coping strategies compared to non-interested students.

The current research reveals a statistically significant inverse relationship between different dimensions of academic stress and adopting the deep learning approach. The most substantial negative correlation was observed with stress arising from a lack of professional knowledge and skills, succeeded by stress associated with the clinical environment, assignments, and workload. Nursing students encounter diverse stressors, including delivering patient care, handling assignments and workloads, navigating challenging interactions with staff and faculty, perceived inadequacies in clinical proficiency, and facing examinations [ 30 ].

In the current study, the multivariate linear regression analysis reveals that various factors positively influence the deep learning approach, including age, female gender, educational year level, and stress from teachers and nursing staff. In contrast, stress from a lack of professional knowledge and skills exert a negative influence. Conversely, the surface learning approach is positively influenced by female gender, educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, but negatively affected by male gender. The models explain 17.8% and 25.5% of the variance in the deep and surface learning approaches, respectively, and both are statistically significant. These findings underscore the intricate interplay of demographic and stress-related factors in shaping nursing students’ learning approaches. High workloads and patient care responsibilities may compel students to prioritize completing tasks over deep comprehension. This pressure could lead to a surface learning approach as students focus on meeting immediate demands rather than engaging deeply with course material. This observation aligns with the findings of Alsayed et al. (2021), who identified age, gender, and study year as significant factors influencing students’ learning approaches.

Deep learners often demonstrate better self-regulation skills, such as effective time management, goal setting, and seeking support when needed. These skills can help manage academic stress and maintain a balanced learning approach. These are supported by studies that studied the effect of coping strategies on stress levels [ 6 , 31 , 32 ]. On the contrary, Pacheco-Castillo et al. study (2021) found a strong significant relationship between academic stressors and students’ level of performance. That study also proved that the more academic stress a student faces, the lower their academic achievement.

Strengths and limitations of the study

This study has lots of advantages. It provides insightful information about the educational experiences of Egyptian nursing students, a demographic that has yet to receive much research. The study’s limited generalizability to other people or nations stems from its concentration on this particular group. This might be addressed in future studies by using a more varied sample. Another drawback is the dependence on self-reported metrics, which may contain biases and mistakes. Although the cross-sectional design offers a moment-in-time view of the problem, it cannot determine causation or evaluate changes over time. To address this, longitudinal research may be carried out.

Notwithstanding these drawbacks, the study substantially contributes to the expanding knowledge of academic stress and nursing students’ learning styles. Additional research is needed to determine teaching strategies that improve deep-learning approaches among nursing students. A qualitative study is required to analyze learning approaches and factors that may influence nursing students’ selection of learning approaches.

According to the present study’s findings, nursing students encounter considerable academic stress, primarily stemming from heavy assignments and workload, as well as interactions with teachers and nursing staff. Additionally, it was observed that students who experience lower levels of academic stress typically adopt a deep learning approach, whereas those facing higher stress levels tend to resort to a surface learning approach. Demographic factors such as age, gender, and educational level influence nursing students’ choice of learning approach. Specifically, female students are more inclined towards deep learning, whereas male students prefer surface learning. Moreover, deep and surface learning approach scores show an upward trend with increasing educational levels and study hours. Academic stress emerges as a significant determinant shaping the adoption of learning approaches among nursing students.

Implications in nursing practice

Nursing programs should consider integrating stress management techniques into their curriculum. Providing students with resources and skills to cope with academic stress can improve their well-being and academic performance. Educators can incorporate teaching strategies that promote deep learning approaches, such as problem-based learning, critical thinking exercises, and active learning methods. These approaches help students engage more deeply with course material and reduce reliance on surface learning techniques. Recognizing the gender differences in learning approaches, nursing programs can offer gender-specific support services and resources. For example, providing targeted workshops or counseling services that address male and female nursing students’ unique stressors and learning needs. Implementing mentorship programs and peer support groups can create a supportive environment where students can share experiences, seek advice, and receive encouragement from their peers and faculty members. Encouraging students to reflect on their learning processes and identify effective study strategies can help them develop metacognitive skills and become more self-directed learners. Faculty members can facilitate this process by incorporating reflective exercises into the curriculum. Nursing faculty and staff should receive training on recognizing signs of academic stress among students and providing appropriate support and resources. Additionally, professional development opportunities can help educators stay updated on evidence-based teaching strategies and practical interventions for addressing student stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions imposed by the institutional review board to protect participant confidentiality, but are available from the corresponding author on reasonable request.

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Acknowledgements

Our sincere thanks go to all the nursing students in the study. We also want to thank Dr/ Rasha Badry for their statistical analysis help and contribution to this study.

The research was not funded by public, commercial, or non-profit organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Rawhia Salah Dogham & Heba Fakieh Mansy Ali

Critical Care & Emergency Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

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Obstetrics and Gynecology Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Asmaa Saber Ghaly

Faculty of Nursing, Beni-Suef University, Beni-Suef, Egypt

Mohamed Mahmoud Seweid

Psychiatric and Mental Health Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Ayman Mohamed El-Ashry

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Ayman M. El-Ashry & Rawhia S. Dogham: conceptualization, preparation, and data collection; methodology; investigation; formal analysis; data analysis; writing-original draft; writing-manuscript; and editing. Heba F. Mansy Ali & Asmaa S. Ghaly: conceptualization, preparation, methodology, investigation, writing-original draft, writing-review, and editing. Nermine M. Elcokany & Mohamed M. Seweid: Methodology, investigation, formal analysis, data collection, writing-manuscript & editing. All authors reviewed the manuscript and accept for publication.

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Dogham, R.S., Ali, H.F.M., Ghaly, A.S. et al. Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study. BMC Nurs 23 , 249 (2024). https://doi.org/10.1186/s12912-024-01885-1

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Relation between stress, time management, and academic achievement in preclinical medical education: A systematic review and meta-analysis

Soleiman ahmady.

Department Medical Education, Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Nasrin Khajeali

1 Deprtment of Medical Education, Fasa University of Medical Sciences, Fasa, Iran

Masomeh Kalantarion

Farshad sharifi.

2 Elderly Health Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

Mehdi Yaseri

3 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of medical Sciences, Tehran, Iran

Identifying the learners' problems is important. Besides, many factors are associated with academic failure, among which time management and stress are more important than any others based on evidence. By using a systematic review and meta-analysis, this study aims to synthesize the findings of studies about the correlation of time management and stress with academic failure to suggest a more in-depth insight into the effect of these two factors on academic failure. Four databases were searched from the inception of January 2018. Publication bias was evaluated visually using funnel plots and sized up by Egger's test. Ninety-four articles were found to be qualified for inclusion after full-text review and additional manual reference made. Of these, 8 were studies of educational interventions that were reviewed in this paper. Regarding the relation of stress and academic performance, the Funnel plot (results not shown) and Egger's test showed no publication bias in the studies ( P = 0.719). Based on this result, the estimated pooled correlation (reverted by hyperbolic tangent transformation) between stress and academic performance was found to be -0.32 (95% confidence interval: -0.38–-0.25). In conclusion, the review recognized a series of potentially mutable medium-to-large correlates of academic achievement, time management, and stress. It would be essential to have experimental data on how easily such self-regulatory capacities can be altered, and these interventions could help students enhance their potential, providing empirical tests for offered process models of academic achievement.

Introduction

Identifying the learners' issues early and offering advice from the start is an essential investment in the training and progress of future practitioners.[ 1 ] The National Committee on Internal Medicine (1999) has described the learner as a trainee who identifies the underlying problems that required to be addressed by a program leader or manager.[ 2 ] Some educators have expressed their concern about difficult learners in case they negatively affect educational programs and other students. Although studies may predict different elements, medical educators would like to be able to predict merely.[ 3 ]

Academic failure is a problem that has turned out to be a central concern for countries in different parts of the world. In order to find the different causes of academic failure, several research projects in this field have been performed. Typically, students experience academic issues with academic and nonacademic characteristics, and the various combinations of reasons for academic failure result in different types of student profiles, suggesting different strategies of intervention.[ 4 ]

The evidence indicates that when intervention techniques are applied for failed students, their performance improves in the subsequent academic year.[ 5 ] Ahmady et al . indicate that failed students can be assisted in becoming successful in the classroom when appropriate intervention techniques are applied. Usually, in research concerning student learning and behavioral outcomes, certain personal attributes of the students are measured, which are then related to some outcome measure. Among these, study skills, such as time management, is one of the factors affecting academic achievement and also stress.[ 6 ]

Personal characteristics are personality, motivation, self-concept, cognitive style, intelligence, and locus of control. Nevertheless, some environmental and contextual difficulties, which lead to unsuccessful learning, are not considered. The purpose of this study is to identify the factors related to the failure of college students.[ 4 ]

Many factors have been related to academic failure.[ 1 ] Ahmady et al . indicate that 21 factors related to academic failure in preclinical medical students, and study skill and stress is reported to be more important among other factors. We have found several studies[ 7 , 8 ] that suggest time management is perhaps more important than any other study strategies.[ 6 ]

West et al . (2011) show that study skills (time management) are usually powerful predictors of first-semester academic performance in medical school and other higher education disciplines.[ 7 ] Practical time management skills are essential. Students who do not plan their time effectively run out of time before running out of the content. Relatively, few studies have investigated the joint contribution of academic performance and study skills.[ 9 , 10 , 11 , 12 ]

Another reason is that medical education is inherently stressful and demanding. An ideal level of stress can increase the level of learning, while over-stress can cause health problems, leading to a decrease in students' self-esteem and failure in their academic competence. A high level of stress can affect the students' learning process in medical school negatively.[ 13 ] Sources of stress include curriculum, personal competence, tolerance, and time outside of medical school. Increased anxiety is associated with increased depression and anxiety.[ 14 , 15 ]

Knowledge about the effective size of these factors (time management and stress) can help policymakers, managers, medical teachers, and counselors track the students' academic failure. It is essential to integrate the evidence produced through all studies to obtain useful information, help medical students, and provide directions for future studies. To the best of the authors' knowledge, this is the first systematic review and meta-analysis of the findings of studies concerning time management and stress associated with academic failure. It suggests a more in-depth insight into the effect of these two factors on the students' academic failure.

Materials and Methods

This systematic review was carried out following PRISMA guidelines.[ 16 ]

Search strategy

PubMed, Web of Knowledge Educational Resources, and Information Center, and Scopus databases were searched.

Using the search No., time limitation was set for searching the resources. For comprehensiveness of the search, the following keywords were used in the abstract, title, and keyword sections: “academic performance” and “academic failure” or “academic achievement” and “drop out;” “medical student” and “struggle student;” “time management” and “stress.” Hand searching was also done in Medical Teacher and Medical Education journals. Furthermore, reference lists of many articles were reviewed to identify the relevant papers. The most celebrated authors in this area were contacted for “gray literature:” conference proceedings, unpublished studies, and internal reports. The obtained data were included in the study. The inclusion criteria for the articles were as follows: being a correlation between study skill and stress with academic performance, observational study design, preclinical medical students, without any language, or time limitation from January 1987 to January 2018.

Inclusion and exclusion criteria

The exclusion criteria for the search were being secondary research or not being a preclinical medical student. All the databases were searched by one reviewer, and Endnote X8 was applied for data management. The articles were imported into Endnote X8 to remove the duplicate data before importing the data into Excel. The imported data were the list of authors, titles, journals, and years of publication. Two team members (N Kh and SA) screened the titles and abstracts to determine the potentially relevant articles. The full-text version of the study was then reviewed if the study met the selection criteria or if there was any doubt concerning the study's eligibility. Furthermore, a third independent researcher was requested to resolve any disagreements.

Quality assessment

The study quality was rated on STROBE guidelines. Over 100 journals have endorsed STROBE guidelines ( http://www.strobe-statement.org ).[ 17 , 18 , 19 , 20 ] Studies were rated for each of the following: title and abstract, introduction, methods, results, discussion, data collection methods, and other information. This yielded a quality rating with a range from 8 to 22.

Data extraction and analysis

As several different variables were tested in each article, thus the article names were repeated. Studies were coded according to author (publication year), effective factors in academic performance, measurement method, type of R, type of analysis, location, and type of study [ Table 1 ]. Two reviewers extracted data from the included articles. They compared extractions and resolved differences through discussion or with a third nonauthors.

Data extraction of articles related to study skill (time management) and stress

SMART=Study management and academic results test

This meta-analysis was conducted via Stata 15.0 software (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). As the distribution of the correlation was highly skewed, the inverse hyperbolic tangent transformation (z = tangh-1(rho) =1/2 ln ((rho + 1)/(rho - 1))) was applied. All the calculations were based on the transformed values. The Cochran's Q test and The I 2 statistic were used to assess and characterize the extent of the heterogeneity, respectively. I 2 -50% was indicated as considerable heterogeneity. Given the high heterogeneity of the data, the random-effects model was used. We used hyperbolic tangent transformation (rho = tangh (z) = [e 2 z - 1]/[e 2 z + 1]) to change the pooled estimates (and its 95% confidence intervals [CI]) to the pooled correlation. All the individual studies results were reported with 95% CIs and demonstrated in a forest plot. Publication bias was evaluated visually using funnel plots and sized up by Egger's test. A P < 0.05 was statistically significant.

The study selection initial database searches retrieved 13,123 articles. After exclusion of duplicate references, conference abstracts, screening titles and abstracts, 6305 articles were selected for further review (title and abstract). A total of 100 articles were found eligible for inclusion after full-text review and additional manual reference screening. Five articles, including the studies of educational interventions, were reviewed in this paper [ Figure 1 ].

Study flowchart demonstrates the inclusion-exclusion process

Study characteristics

Study setting and populations.

Most of the studies were completed in Europe (50%), 2 (25%) USA, and 2 (25%) Asia.

Type of design

The majority design in the articles was prospective, followed by correlational [ Table 1 ].

Aims of studies

The purpose of the studies was to report the effect level of the study skill (time management) and stress on academic performance.

Regarding the relation of stress and academic performance, the Egger' test and Funnel plot (results not shown) indicated that there was no publication bias in the studies ( P = 0.719). The same was obtained when we evaluated the relation of the study skill (time management) and academic performance, not statistically significant ( P = 0.833).

The individual studies transformed between stress and academic performance were shown in a forest plot [ Figure 2 ]; based on this result, pulled correlation (result from hyperbolic tangent transformation) between stress and academic performance was found to be – 0.32 (95% CI [-0.38, -0.25]).

Correlation between stress and academic failure

The individual studies transformed between study skill (time management) and academic performance were demonstrated in a forest plot [ Figure 3 ]; based on this result, pulled correlation (result from hyperbolic tangent transformation) between stress and academic performance was found to be 0.39 (95% CI [0.29, 0.47]).

Correlation between study skill (time management) and academic failure

To the authors' knowledge, this is the first systematic review and meta-analysis of the evidence concerning the effect of study skill (time management) and stress on academic performance.

Overall, with this review, we found medium to high-quality evidence from a modest number of studies, suggesting that study skills (time management) and stress significantly affect academic achievement: study skill (time management) (ES: 0.39) and stress (ES: -0.32).

However, research suggests that study skills (time management) are also significant factors affecting academic achievement in medical schools.[ 8 , 21 , 22 , 23 , 24 , 25 ]

Study skills are one of the more reliable predictors of first-semester total grades.[ 7 ] The predictive strength of first-semester final average is accounted for by scores on time management,

Teaching time management rules, such as preventing postponement, previewing data, reviewing material shortly right after presented, prioritizing items, handling study periods, reviewing repeatedly, and making time for other commitments, is an essential component.[ 26 ]

For instance, sometimes, students procrastinate studying material they have problem with or do not see the applicability of. In this instance, seminars or counseling, which concentrate on arranging these projects for one's optimum time of day such that it will be simpler to focus on the material and reduce procrastination, may be offered.[ 27 ]

Time management aims to improve the nature of activities that require a limited time. The inability to use time in the learning process is the main problem for the students. Previous studies have shown that the excessive intensity of courses affects productivity negatively. In this situation, medical students, who have to cope with an intensive training curriculum, may inevitably but efficiently make the most of their time. To succeed in the education process, medical students must set goals for their education and plan for appropriate academic progress. They, therefore, have to follow course schedules, be prepared for examinations, and use the time available for other activities.[ 28 ]

Another significant issue is that there is a substantial increase in stress levels during study times, in the 1 st year in particular.[ 29 ] Perceived stress is a key factor in discriminating among students with low versus high academic performance.[ 30 ] First-year students face different challenges that can be seen as potential stressors. They have to get familiar with a new environment, get into contact with other students, choose their lectures and seminars, participate in extracurricular activities, and manage their first tests. Another source of students' perceived stress is time-related demands, such as an increasing workload, time pressure, and regulation of their self-study.[ 31 ]

Pfeiffer notes that too much stress is negatively associated with students' readiness, focus, and performance, while positive stress helps the student achieve maximum performance.[ 32 ] It should also be recommended that this situation is the first exam in which students are exposed to a significant amount of integrated curriculum. Often, students are suggested by their seniors to pursue an education in the coming years; thus, they can lower the stress levels, control stress in a better way, and enhance their academic performance.

Managing self-efficacy, flexibility, and social support also are related to academic achievement; thus, intervening to enhance self-efficacy, resilience, and social support may lessen the perception that stress is affecting performance.

Limitations

The limitation of this review is that statistically significant time management and stress have not been reported in all studies.

Conclusions

This review of 31 years of research on the correlation of stress, time management, and academic failure has been devoted to the understanding of the effect of time management and stress on academic achievement of medical students. This systematic review and meta-analysis are the first in the field. We wish that this work provides a base for more focused research and intervention. Finally, our review and others have identified a series of potentially modifiable medium-to-large correlates of academic achievement, time management and stress in particular. It would be worthful to have experimental data on how easily such self-regulatory capacities can be altered, as well as for whom, over what period, and to what extent do such changes to be effective academic performance. These interventions could help students develop their potential and would provide empirical tests for proposed process models of academic achievement.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgments

The authors would like to thank all of authorities and students at Medical School in Shahid Beheshti University of Medical Sciences for their assistance.

2024 Theses Doctoral

Ice formation, deformation, and disappearance

Case, Elizabeth

From the moment a snowflake touches down on the surface of a glacier, it begins a process of transformation. Fresh snow, made up of single-grained snowflakes is compacted into glacial ice by the weight of subsequent snowfall and by sintering, grain boundary sliding and diffusion. At first, snow grains accommodate the stress through mechanical failure and by changing their shapes and positions. Fragile, dendritic structures on the edges of snowflakes break off, and grains round into lower free energy configurations. Rounded grains slip into air pockets. As time passes, increasing overburden of a load to bear, and it is, for a single snowflake. But it is precisely this stress that creates a glacier. Stress, in this case, is a catalyst for transformation. But don't worry. I am not going to make an overly forced metaphor for what happens during a doctorate program.} Pressure causes the grains to merge, large grains absorbing small ones. As ice grains squeeze and grow into all the available pore space, grains trap air bubbles and cut them off from the atmosphere, preserving records of climate conditions. Eventually, these processes densify the snow so thoroughly that it metamorphoses into glacial ice, and from a crumbly collection of snowflakes emerges a cohesive crystalline matrix. This process, firn densification, is the subject of my first chapter. From measurements of englacial strain rates by repeat phase-sensitive radar deployments, we show it is possible to extract densification rates that match modeled predictions. The formation of ice is just the beginning of the story of a glacier. As and after ice forms, gravity pulls on the body of the glacier; ice flows under its own weight, becoming a viscous river that meanders from high elevations toward the sea level. Along the way, various other forces act on the ice (e.g., friction at the ice-bed causes ice to shear, narrowing valley walls create compressive stresses, etc.). This history can be written into the ice in the orientation and configuration of its molecular structure. Ice is made of a regular crystal matrix of water molecules. Covalently bonded oxygen and hydrogen molecules assemble into sheets of hexagons, held to each other by hydrogen bonds. The relative orientation of these hexagonal sheets is called the "ice fabric”, and its importance lies in the fact that ice’s asymmetric molecular structure gives rise to asymmetric properties. For example, ice is softer—more deformable—when stress is applied parallel to the hexagonal planes, like playing cards sliding over one another. Over hundreds or thousands of years, this asymmetric response to stress causes the hexagonal planes to rotate so that they lie perpendicular to the direction of compressive stress. This, in turn, changes which relative direction a glacier is the “softest”. In short, the history of the glacier is written into its fabric. Ice remembers the stress it has undergone, and that memory changes its resistance to (or accommodation of) stress in the present and future. In chapter two, I use an autonomous phase-sensitive radar to measure the ice fabric along a central transect of Thwaites Glacier. Thwaites drains ice from West Antarctica and is one of the fastest changing glaciers on the continent. Locked up in Thwaites is at least half a meter of sea level rise, as well as much of the buttressing that holds back WAIS. Measurements of the fabric of Thwaites tell us about the history of stress undergone by the glacier, as well as any change in relative direction of the "softest" ice. As a glaciologist, I have dedicated my life to studying how glaciers form, flow, and disappear. As an artist and writer, I am interested in material memory, with a particular orientation toward ice itself and in the way the language and mathematics used to describe ice mimic processes that happen in body, mind, and society. My fourth chapter is centered on the creative research and art produced during my dissertation, particularly focused on a visual autoethnography of my body I created during my first field season in Antarctica in 2022-2023. In it, I try to grapple with whether/how, even as positivist science demands I remove as much of myself as possible from my scientific research, my body/myself show up in small ways in my data. I consider how ice's response to stress—to soften or harden, to flow or crack—is in many ways, a mirror for how we as humans respond to stress. Other work in Chapter 4 was created in direct response to the beauty of glaciated landscapes and the grief I struggle to manage in response to their rapid change. Biome I is a short zine that uses faux-color satellite imagery overlain with text and meshes of glaciers from Grand Teton National Park (GRTE). In 2021, I spent six months as a Scientists-in-Parks fellow through AmeriCorps, joining the park's physical science team in Wyoming to expand their glacier monitoring program. From this work emerged Chapter 3 a history of glacial change in the park over the last 70 years from in situ and remotely sensed observations. This work, while quite different from my previous scientific output, allowed me to learn and explore other glaciological techniques as well as template methodologies and provide information that is immediately useful for education and action in GRTE and other rapidly deglaciating landscapes. Much of the way I have come to understand glacial geophysics is by considering the ways they connect more broadly to our lived experiences. In the Tetons, this involved understanding how deglaciation affects the park's ecological systems and the evolving safety for visitors given the changing ice conditions. In pursuit of both expanding my own understanding and hoping to share with others the joy and beauty of the study of ice, I have developed numerous education efforts to make the study of glaciers, climate, and the earth physical, tangible, less abstract, emotional, joyful, and intuitive. Chapter 5 concludes the thesis by taking a step back to look at education and teaching, the thread that has carried through my doctorate, from prior to starting graduate school and, I hope, that will continue long after. I discuss the influences of teacher-philosophers like Shannon Mattern, Lynda Barry, and bell hooks, who have all, in their own way, striven to reshape the (idea of the) classroom into forms that better serve the learner. This work has taken place on the seat of a bicycle riding across the country, on an icefield in Juneau, Alaska, and in my own backyard, in classrooms across New York City. To conclude, I hope this thesis is not only a scientific effort, but one that draws the curtain back on the broader work we do as glaciologists. We are also artists and educators, caretakers, archivists, and public figures. Our work can be physically, mentally, and emotionally demanding, and it is as often full of grief as it is of awe.

Geographic Areas

• Wyoming--Grand Teton National Park
• Alaska--Juneau
• New York (State)--New York
• Physical geography
• Glaciers--Climatic factors
• Glaciologists
• Climatic changes
• Environmental sciences--Study and teaching
• AmeriCorps (Program : U.S.)

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HIGH SOLIDS LOADING AQUEOUS SLURRY FORMATION OFCORN STOVER BEFORE PRETREATMENT IN A FED-BATCH BIOREACTOR

Feedstock variability represents a challenge in the adoption of lignocellulosic biomass for biofuels and biochemicals production, due to the differences in critical chemical and physical properties like lignin content, and water absorption respectively. Thus, difficult continuous manufacturing processes in biorefineries, hinder the transition from liquid feedstocks to renewable materials that consisting of solid particles. Modeling of flow properties based on rheological measurements of treated biomass is a quantitative metric for identifying if different feedstocks form pumpable slurries. Additionally, the correlation of yield stress to physical and chemical properties gives a measure that accounts for the variability in the processing design. This research models rheological properties and relates these to compositional data from different non-pretreated fractions of corn stover biomass slurries. Slurries were formed with solids concentrations of 300 g/L in a 6 hours fed-batch process using the commercial enzymes Celluclast 1.5L or Ctec-2 at 1FPU/g or 3 FPU/g of dry solids, basis to enable the liquefaction (i.e., slurry-forming) mechanism. We found that insoluble lignin content of the different fractions was related to water absorption in pellets and free water on slurries and that free water was a good indicator of the potential for a material to form slurry. Higher flowability (lower yield stress) was found at higher content of lignin, particularly for materials containing 26% lignin where yield stress was reduced to 254Pa when compared with mixtures of 14% lignin that presented yield stresses of around 4000 Pa. We show that rheology modeling linked to compositional characteristics for biomass slurries can be used to predict material flow behavior in a biorefinery to optimize and achieve high solids loadings that enhance the production of ethanol for biofuels. This insight and the ability to form high concentration slurries before pretreatment holds the potential to develop new processing strategies that could help to foster a more efficient and sustainable bio-based industry.

DOE EE0008910

Purdue university college of agriculture idea challenge 2030, degree type.

• Doctor of Philosophy
• Agricultural and Biological Engineering

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, additional committee member 5, usage metrics.

• Bioprocessing, bioproduction and bioproducts
• Crop and pasture biomass and bioproducts
• Agricultural engineering