literature review for stress management

Stress Management Interventions for Nurses: Critical Literature Review

Affiliation.

• 1 Mayo Clinic.
• PMID: 31014156
• DOI: 10.1177/0898010119842693

Background: The nursing literature contains numerous studies on stress management interventions for nurses, but their overall levels of evidence remain unclear. Holistic nurses use best-available evidence to guide practice with self-care interventions. Ongoing discovery of knowledge, dissemination of research findings, and evidence-based practice are the foundation of specialized practice in holistic nursing. This literature review aimed to identify the current level of evidence for stress management interventions for nurses. Method: A systematic search and review of the literature was used to summarize existing research related to stress management interventions for nurses and recommend directions for future research and practice. Results: Ninety articles met the inclusion criteria for this study and were categorized and analyzed for scientific rigor. Various stress management interventions for nurses have been investigated, most of which are aimed at treatment of the individual versus the environment. Contemporary studies only moderately meet the identified standards of research design. Issues identified include lack of randomized controlled trials, little use of common measurement instruments across studies, and paucity of investigations regarding organizational strategies to reduce nurses' stress. Conclusion: Future research is indicated to include well-designed randomized controlled trials, standardized measurement tools, and more emphasis on interventions aimed at the environment.

Keywords: burnout; nurse; resilience; stress management; systematic review.

Publication types

• Nurses / psychology*
• Nurses / statistics & numerical data
• Stress, Psychological / psychology
• Stress, Psychological / therapy*

• 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.

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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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Correlates of teachers’ classroom management self-efficacy: A systematic review and meta-analysis

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This meta-analysis examined literature from the last two decades to identify factors that correlate with teachers’ classroom management self-efficacy (CMSE) and to estimate the effect size of these relationships. Online and reference list searches from international and Chinese databases yielded 1085 unique results. However, with a focus on empirical research the final sample consisted of 87 studies and 22 correlates. The findings cluster the correlates of CMSE into three categories: teacher-level factors (working experience, constructivist beliefs, teacher stress, job satisfaction, teacher commitment, teacher personality, and teacher burnout), classroom-level factors (classroom climate, classroom management, students’ misbehaviour, students’ achievement, classroom interaction, and student-teacher relationship), and school-level factors (principal leadership and school culture). The results of this meta-analysis show small to large correlations between these 15 factors with CMSE. How these factors are associated with teachers’ CMSE and recommendations for future CMSE research are discussed.

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Introduction

Classroom management is frequently defined as “the actions teachers take to create an environment that supports and facilitates both academic and social-emotional learning (p.4)” (Evertson & Weinstein, 2006 ). There is a consensus that classroom management no longer simply refers to responses towards student misbehaviour, rather it is serves as “an umbrella term for an array of teaching strategies that enhance effective time use in class (p.2)” (Lazarides et al., 2020 ). Effective classroom management is highly related to students' academic, behavioural, and social-emotional outcomes (Korpershoek et al., 2016 ), as well as teachers' wellbeing (Sutton et al., 2009 ). To effectively manage classrooms, teacher must possess professional knowledge, skills, and efficacy beliefs in their classroom management capability (Main & Hammond, 2008 ). Classroom management self-efficacy (CMSE) is a teacher’s belief about his or her capabilities to organize and execute the courses of action required to create a positive learning environment that supports successful student learning outcomes (Lazarides et al., 2018 ).

Self-efficacy is a self-perception of one's capacity to accomplish a certain task (Bandura, 1977 ), which has been well represented in educational research. Teacher self-efficacy (TSE) refers to a teacher’s belief of his or her capabilities to perform a specific teaching task successfully in a particular teaching context (Tschannen-Moran et al., 1998 ), with a growing acknowledgment of its influence on important outcomes for teachers and students (Klassen & Tze, 2014 ). According to Bandura ( 1997 ), individual’s self-efficacy is reflected and evaluated through interpreting information from four sources, namely mastery experiences , vicarious experiences , social persuasions , and emotional arousal . Tschannen-Moran et al. ( 1998 ) proposed an integrated model of teacher self-efficacy applying these four sources of efficacy information. They suggested that the development of teacher self-efficacy is cyclical with teachers’ interpretations of efficacy-relevant information affecting teacher self-efficacy. This in turn has an impact on the setting of teaching objectives, teaching effort and persistence in managing challenging situations. The performance of completing a teaching task, either successfully or not, becomes a source of new efficacy information, which may have either positive or negative effects on TSE.

Of the four sources of influence on TSE, mastery experiences, which involve teachers achieving their desired goals, are often viewed as the strongest predictor of TSE (Usher & Pajares, 2008 ) TSE in the school context has been linked to students’ successful academic achievements and positive classroom climate (Klassen & Tze, 2014 ). Vicarious experiences including the observation of highly effective teachers or noting students’ preferred teachers provide another source of influence on teachers’ TSE. Individual’s TSE may be influenced either positively or negatively as they engage in reflecting on their own personal teaching competence or relationships with their students in comparison to those of colleagues. Social persuasions, often in the form of feedback from experts or students, is a particularly powerful influence for preservice and novice teachers (individuals with little prior experience) as they can either develop more confidence or doubt their capacities to be a successful teacher (Tschannen-Moran & Hoy, 2007 ). Finally, emotional arousal is elicited by stress, anxiety and tension and can affect individual’s perceived self-efficacy when coping with tough demanding situations, for example, novice teachers receiving either positive or negative feedback from leaders or reactions from students and/or parents (Marschall, 2023 ; Morris et al., 2017 ). Together these four sources, being both intrinsic and extrinsic, generate either positive or negative self-efficacy in a specific teaching context.

The specificity of context is important to individual’s TSE. Although teachers might feel efficacious in one area of instruction or with one group of students, they may also report low confidence in different areas or student group (Tschannen-Moran & Hoy, 2001 ). Acknowledging the context-specific nature of TSE, some researchers began exploring TSE in specific areas, such as science teaching (Riggs & Enochs, 1990 ), language and literacy teaching (Cantrell & Callaway, 2008 ), mathematics teaching (Bardach et al., 2022 ), special/inclusive education (Coladarci & Breton, 1997 ; Woodcock et al., 2022 ), teaching with technology (Alt, 2018 ), and classroom management (Dicke et al., 2014 ; Emmer & Hickman, 1991 ; Hettinger et al., 2021 ; Lazarides et al., 2018 ; Tschannen-Moran & Hoy, 2001 ). Tschannen-Moran et al. ( 1998 ) noted that determining an optimal specificity level of TSE is challenging. TSE measures are most useful and generalizable when measures refer to specific teaching activities and tasks, but their predictive power is limited to specific skills and contexts. Classroom management, a critical teaching skill domain rather than a particular context (e.g., teaching science), has therefore attracted the attention of TSE researchers (O'Neill & Stephenson, 2011 ). Aloe et al. ( 2014 ) examined the relationship between CMSE and teacher burnout, acknowledging that CMSE is a domain specifical construct.

CMSE has already been identified as a distinct dimension of TSE, both for pre-service teachers (Emmer & Hickman, 1991 ) and in-service teachers (Tschannen-Moran & Hoy, 2001 ). To measure self-efficacy for classroom management, some researchers (e.g., Brouwers & Tomic, 2000 ; Hettinger et al., 2023 ) used sub-scales of TSE scales, such as Teachers’ Sense of Efficacy Scale (TSES) (Tschannen-Moran & Hoy, 2001 ), and Teacher Efficacy Scale (TES) (Emmer & Hickman, 1991 ). Specific scales investigating CMSE, however, have also been developed. For example, the Behaviour Management Self-Efficacy Scale was designed to measure preservice teachers’ self-efficacy of classroom management (Main & Hammond, 2008 ). The items used to measure CMSE were mainly for maintaining order and control in classrooms and facilitating student socialisation and cooperation. Whereas the other aspects of classroom management, establishing and enforcing rules, gaining and maintaining engagement, and resources allocation, were less represented in CMSE measurements (O'Neill & Stephenson, 2011 ).

Research findings identified that teachers with high levels of CMSE show more interests in using student-centred strategies to approach problems (Emmer & Hickman, 1991 ), hold more humanistic classroom management beliefs (Woolfolk & Hoy, 1990 ), feel more empowered to help students in social-emotional (Reilly, 2002 ) and academic areas (Lazarides et al., 2018 ) as well as in the area of classroom behaviour (Dicke et al., 2014 ). However, high general TSE do not ensure high levels of CMSE. Understanding the factors of influence on teachers’ CMSE is important for eductaion practitioners, policy development, preservice teacher programs and researchers. Therefore, this article examines the factors that serve to shape CMSE and to provide an overview of the current research about CMSE and how CMSE could be improved.

To date, there have been multiple reviews of global TSE conducted in reviewing several distinct areas: the measurements of TSE (Tschannen-Moran et al., 1998 ); summarizing key issues surrounding the TSE research (Klassen et al., 2011 ); examining the effectiveness of interventions on TSE (McArthur & Munn, 2015 ); synthesizing the research exploring the relationship between TSE and teaching effectiveness (Klassen & Tze, 2014 ; Tschannen-Moran et al., 1998 ), and teacher burnout (Brown, 2012 ). However, limited attention has been paid specifically to reviewing research about CMSE with O'Neill and Stephenson ( 2011 ) conducting a comprehensive review of CMSE items and scales and Aloe et al. ( 2014 ) examining the evidence of CMSE in relation to teacher burnout. By reviewing factors that correlate with CMSE, our paper also makes contributions to TSE theory and clarifies the special features of TSE in specific area of classroom management.

Conceptualizing the review

To frame our conceptual understanding of CMSE, we draw on the three categories of factors related to TSE as defined by Fackler et al. ( 2021 ) and explore in more depth some of these factors as they align to recent research. As presented in Fig. 1 , the proposed conceptual framework guided our synthesis of the literature, which indicated that factors associated with CMSE can be divided into three main strands: (1) personal characteristics of teacher (teacher-level factors); (2) characteristics of classroom composition (classroom-level factors); (3) teachers’ working conditions (school-level factors).

Conceptual Framework for Synthesizing Empirical Research on CMSE

First, the conceptual framework includes teacher personal characteristic, facilitating our understanding of how teacher background (e.g., gender, age, working experience, educational level) influences teachers’ beliefs towards the area of classroom management. Previous studies suggest that there is no age effect on teachers’ CMSE (Ford, 2019 ; Hicks, 2012 ; Lazarides et al., 2020 ; Lee & van Vlack, 2018 ). Unlike age, mixed results were found for the relationship between other demographic variables and CMSE. Some found a positive relationship for female teachers (Calkins et al., 2021 ; Zee et al., 2016 ), for male teachers (Hettinger et al., 2021 ; Tran, 2015 ), but also no gender effect (Lazarides et al., 2020 ). Some found teacher education level had a positive relationship with CMSE (Hu et al., 2021 ; Valente et al., 2020 ) but some found a negative relationship (Fackler et al., 2021 ). A higher level of CMSE was identified for more experienced teachers (Brouwers & Tomic, 2000 ; Klassen & Chiu, 2010 ), whereas no changes in CMSE were found from early until mid-career teachers (Lazarides et al., 2020 ).

In addition to demographic variables, many studies have examined the relationship between teachers’ CMSE and psychometric constructs. Self-efficacy has been viewed as a protective factor for teacher against psychological strain (Lazarides et al., 2020 ; Schwerdtfeger et al., 2008 ). Teachers who perceive they have sound ability to manage the classroom are less prone to increased stress levels. The findings of extant research are as expected indicating that CMSE is negatively related to psychological strain (teacher stress, teacher burnout) (Brouwers & Tomic, 2000 ; Eddy et al., 2019 ; Vidic et al., 2021 ; Williams, 2012 ) and positively related to teachers’ wellbeing (job satisfaction, teacher commitment) (Dicke et al., 2018 ; Klassen & Chiu, 2010 ; Liu et al., 2018 ; Miller, 2020 ; von der Embse et al., 2016 ).

Personality traits are general behavioural tendencies that may influence how efficacy information is evaluated and/or have an effect on people’s behaviour, and in turn, influence the evaluation of self-efficacy (Baranczuk, 2021 ). The Big Five, known as the most widely accepted taxonomy of personality traits, consists of neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness (McCrae & Costa, 2003 ). Several studies have also examined the relationship between CMSE and teacher’s personality assessed by the Big Five Inventory suggesting that openness and extraversion have positive relationships with CMSE (Bullock et al., 2015 ; Rahimi & Saberi, 2014 ). Teachers’ constructivist beliefs, which refers to “the ways they (teachers) believe students learn best and how they as teachers might facilitate this learning” (OECD, 2014 , p.165), have also shown a positive relationship with CMSE (Berger et al., 2018 ; Fackler et al., 2021 ).

Turning to the second category, researchers have examined several characteristics of classroom composition, ranging from class setting to teachers’ behaviour towards student, and students’ outcomes. It was suggested that a bigger class size was associated with a high level of teacher global self-efficacy (Raudenbush et al., 1992 ). However, for teacher self-efficacy in classroom management in particular, a negative association with large class size was found (Kunemund et al., 2020 ). Self-efficacy beliefs has been suggested to affect individuals’ behaviours at different level, from initial choice of behaviour type to the amount of effort and the extent of persistence in the implementation process (Bandura, 1977 ). CMSE theoretically acts as a mediator factor between teachers’ knowledge and practice towards the area of classroom management. Positive aspects of classroom management (Chen et al., 2020 ; Lazarides et al., 2020 ) were positively related to CMSE. On the other hand, the success that teachers achieve in the classroom (mastery experiences) such as good classroom climate (Guangbao & Timothy, 2021 ), students’ achievement (Hassan, 2019 ), high quality classroom interaction (Ryan et al., 2015 ), and positive student-teacher relationship (Zee et al., 2017 ) were suggested to inform teachers’ CMSE.

According to social cognitive theory, human behaviour is a product of the interaction between personal processes and the external environment (Bandura, 1986 ). School-level factors representing, to some extent, the environment and conditions in which teachers work, have increasingly gained the attention of researchers of CMSE. Similar to teachers, the relationship between general teacher self-efficacy and principal demographics (e.g., gender, age, working experience) has been examined. For TSE in classroom management in particular, mixed results were found for principal gender, with a positive association for male principals (Fackler et al., 2021 ), but also no effect based on principal gender (Ford, 2019 ). In regard to time-related characteristics like principal age and working experience, a negative association was found (Fackler et al., 2021 ). Another factor that has been reported by many studies (Bellibas & Liu, 2017 ; Buentello, 2019 ; Fackler et al., 2021 ; Ford, 2019 ; Holzberger & Prestele, 2021 ) to have a significant impact on teachers' perceptions of CMSE is principal leadership. For school organizational characteristics, some studies (McLeod, 2012 ; Öztürk et al., 2021 ) have examined the relationship between school culture and CMSE, some (Fackler et al., 2021 ; George et al., 2018 ) have examined CMSE between teachers in private school and public school, while some (Fackler et al., 2021 ; Looney, 2004 ) have examined CMSE for teachers located in different school size (number of student enrolments).

Overall, the conceptual framework depicted in Fig. 1 captures correlates of CMSE that are both theoretically and empirically tested. In light of the ongoing inconsistency of findings in these studies, we then provided a meta-analysis of CMSE, outlining the extent to which it is correlated with teacher-level factors, classroom-level factors and school-level factors. We also used this framework to guide the discussion and to clarify the areas that need further exploration.

Literature Search

This meta-analysis selected empirical research results published in international and Chinese databases between 2000 and 2021. We searched commonly used social science databases (e.g., ERIC, Web of Science, Academic Search Complete, PsycArticles and PsycINFO). Chinese literature was searched mainly from CNKI (China National Knowledge Infrastructure), Wanfang, and Weipu Database. Given the limited number of studies that solely focussed on CMSE, we used the descriptors: " self-efficacy " and/or " efficacy " as keywords, and “ teacher ” and " classroom management " and/or " behaviour management " as abstract search terms. The searches were restricted to return peer-reviewed articles and dissertations (conference papers, books and book chapters were excluded as the review process can be non-standard). In addition to searching databases, we also included an examination of reference lists of the existing reviews of CMSE (Aloe et al., 2014 ; O'Neill & Stephenson, 2011 ).

Eligibility Criteria

The primary studies eligible for inclusion in this meta-analysis met the following criteria: (1) the study measured teacher self-efficacy for classroom management; (2) the study examined the relationship between at least one factors and teachers’ CMSE instead of just reporting descriptive data of CMSE; and (3) the study reported statistical data (e.g., Pearson r , sample size) to quantify the relationship between a factor of interest and teachers’ CMSE. Moreover, given evidence that classroom management content differs between general and special education (Stough, 2006 ) this meta-analysis exclusively examines studies in which (4) the sample comprised of in-service teacher in mainstream school settings. Finally, to facilitate the meta-analysis, (5) correlates included in two or more studies were included in the meta-analysis.

Selection and Inclusion of Studies

In the first phase of the literature search a total of 1,386 Chinese and English articles were identified using the above search strategy, and 301 duplicates were excluded, resulting in a total of 1085 articles. We went through a three-phase process to screen primary studies included in this meta-analysis as illustrated by Fig. 2 . First, 1,085 studies were examined by reviewing the titles and the abstracts, 756 studies were qualitative research, or not focused on CMSE so were not appropriate to include in the study.

PRISMA flow diagram. Adapted from Moher et al., ( 2009 )

In phase two, 329 articles were left for full-text reading to ensure that studies reported clear, explicit, and complete data on the findings of the research. The result of this examination found that 81 of the research studies in the pool were appropriate, and 248 were found not to be suitable (n=15, qualitative analysis; n=13, full-text not available; n=60, missing data; n=38, reported global TSE; n=17, not reported CMSE; n=76, less than two studies focusing on related variables; N=29, miscellaneous reasons).

In phase three, we backwards searched eligible articles in the reference lists of previous reviews (Aloe et al., 2014 ; O'Neill & Stephenson, 2011 ) and included six additional studies. Of these six studies, the abstracts of five (Bumen, 2010 ; Huk, 2011 ; Skaalvik & Skaalvik, 2007 ; Williams, 2012 ; Yoon, 2002 ) did not mention the term “classroom management” or “behaviour management” and one (Ozdemir, 2007 ) was not included in commonly used databases, resulting in the omission of these literature from our retrieval. Hence, the present meta-analysis included a sample of 87 primary studies (see supplementary information  for details of studies selected).

Study Coding

Studies that met all inclusion criteria were reviewed and all factors associated with teachers’ CMSE in these studies were coded. Studies were coded according to year of publication, publication type, country of origin, school level, CMSE measurement, sample size and reported effect size. Since the primary goal was to synthesize estimates of the relationship between teachers’ CMSE and various factors, the primary effect size we coded was Pearson r. In order to include as many studies as possible, several formulas were used to compute effect sizes (Pearson r):

(1) If only Spearman r was reported in studies, we converted it to Pearson r using the following equation \({r}_s=\frac{6}{\pi }\ {\mathit{\sin}}^{-1}\frac{r}{2}\) (Xiao et al., 2021 ).

(2) If only a β coefficient was reported in studies (β∈(−0.5, 0.5)), then the following equation was used r  =  β  ∗ 0.98 + 0.05( β  ≥ 0); r  =  β  ∗ 0.98 − 0.05( β  < 0) (Peterson & Brown, 2005 ).

(3) If only a t-test value was reported in studies, then the following equation was used \(r=\sqrt{\frac{t^2}{t^2+ df}}\) (Card, 2012 ).

(4) If studies conducted an ANOVA between two groups (i.e., F (1, df ) ), then the following equation was used \(r=\sqrt{\frac{F_{\left(1, df\right)}}{F_{\left(1, df\right)}+ df}}\) (Card, 2012 ).

(5) If x 2 with 1 degree of freedom was reported in studies, then the following equation was used \(r=\sqrt{\frac{x_{(1)}^2}{N}}\) (Card, 2012 ).

Coding for study characteristics and effect sizes was done by the first author. A randomly selected sample of 50% of the included studies ( K =44) were coded a second time by the first author to establish intracoder reliability (Wilson, 2019 ). Estimates of intracoder reliability were recorded for each variable. The agreement rate was higher than 95% for all variables of interests.

In addition, given that more than one correlation value were reported in some studies, two approaches were used in the determination of which one was to be included in this meta-analysis: (1) if the correlations were independent (e.g., Wettstein et al., 2021 ), all the correlations were included in the analysis and were considered to be independent studies, and (2) if the correlations were dependent (e.g., Lazarides et al., 2020 ), then the highest correlation value was recorded.

Meta-Analytical Procedure

This meta-analysis was conducted with the aid of Comprehensive Meta-Analysis version 3.0 Footnote 1 . Pearson's correlation coefficient r was determined to be the effect size in this study. Specifically, r values were firstly converted to Fisher’s Z scale,

then the transformed values were used to calculate the aggregated correlation coefficients, and finally we converted summary Fisher’s Z back to correlation coefficients r to obtain the final overall effect sizes (Borenstein et al., 2009 ).

If the original study only reported r coefficients for each dimension of the variable (e.g., Eddy et al., 2019 ), these coefficients were convert to Fisher’s Z to compute mean scores, and then converted back to r values (Yali et al., 2019 ). A random effect model was used for this meta-analysis because substantial variation exists across studies in terms of various factors that may correlate with teachers’ CMSE.

The following indicators were taken into account in this analysis:  k (the number of studies included in the meta-analysis), r (the average effect size expressed in Cohen’s index (Cohen, 1988 ), with values around 0.1 considered a small effect, around 0.25 a medium effect, and 0.4 or higher a large effect), lower limit and upper limit effect sizes (the values of the 95% confident interval), Z values (statistical test for the null hypothesis regarding the average effect size), and the indicators of heterogeneity, namely Q and I 2 .

The Q test is used to test whether the total heterogeneity of the weighted mean effect sizes was statistically significant. The I 2 index provides estimates of the degree of inconsistency in the observed relationship across studies, and values of 0.25, 0.5, and 0.75 indicate low, medium, and high levels of heterogeneity (Borenstein et al., 2009 ). For heterogeneity, we performed moderator analysis. Given evidence that classroom management content differs at different school level (Evertson & Weinstein, 2006 ) moderators tested here included school level. In addition to sample characteristic, the characteristics of the study itself are often also responsible for heterogeneity. Hence, we also included year of publication, publication type, and country of origin as moderators. In the first step in this process subgroup analysis was used for categorical moderators (publication type, country of origin, and school level), which estimated synthetic effects for each category. Specially, we used a Q-test based on analysis of variance to compare subgroups. At the second step, for a non-categorical moderator (year of publication), meta-regression analysis was used to test if the variable was a significant covariate within the meta-regression model.

Characteristics of Included Studies

Based on the eligibility criteria above, 87 primary studies were included in the meta-analysis (see Table 1 ). It is worth noting that most (87.36%) of these eligible 87 studies were published between 2010-2021 and over half (68.97%) of the 87 studies included in this meta-analysis were published in peer-reviewed journals. As can be seen from Table 1 , research on teachers’ CMSE included here was most frequently undertaken in the USA (n=32). Almost half of the included studies (n=49) were conducted with a sample size between 100 and 500 observations. Additionally, three studies used extensive data with more than 100,000 observations (Bellibas & Liu, 2017 ; Fackler et al., 2021 ; Yuan & Jinjie, 2019 ). As for the school level, most studies were conducted with middle school (19), elementary school (14), high school (14), and pre-kindergarten (4) teachers. There were also 4 studies focused on higher education and one was designed for vocational education (Berger et al., 2018 ). The vast majority of studies (n=61) have used the classroom management sub-scale of the Teachers’ Sense of Efficacy Scale (TSES) (Tschannen-Moran & Hoy, 2001 ) or its adapted version to access teachers’ CMSE, either its long form (e.g., Sims et al., 2021 ) or short form (e.g., Guangbao & Timothy, 2021 ).

Overall meta-analytic effect sizes

The analysis included 87 samples, 189 correlations, and a total listwise sample size over 480,000. Overall, 22 factors that correlated with CMSE have been generated from these 87 studies (see details of which factors were included in which studies in the  supplementary information ). The results of this meta-analysis show small to large correlations between 15 factors and CMSE (see Tables 2 , 3 , 4 ).

Teacher-level Factors

Table 2 presents the overall effects of the association between various teacher-level factors and teachers’ CMSE. Through the eligibility criteria, the present study identified four teacher demographic variables (See Panel A of Table 2 ). Our results showed no evidence that in-service teachers’ sense of self-efficacy in the area of classroom management varied with age, or gender, or educational level. We did find that teachers’ CMSE was positively correlated with teachers’ working experience, which indicated that more experienced teachers hold higher level of CMSE.

In addition to teacher demographic variables, the present study also identified six psychometric constructs of teachers (See Panel B of Table 2 ). Our results showed that all these six factors were significantly correlated with CMSE. Overall, the strongest correlation with CMSE was personal accomplish PA ( r =0.415, CI [0.318, 0.504]). Openness showed the largest correlation among the Big Five personality traits ( r =0.220, CI [0.135, 0.303]), followed by extraversion ( r =0.212, CI [0.121, 0.298]) and conscientiousness ( r =0.121, CI [0.033, 0.207]), whereas agreeableness and neuroticism had no relationship with teacher’ CMSE. Moderate correlations were observed for job satisfaction ( r =0.302, CI [0.255, 0.347]), teacher commitment ( r =0.371, CI [0.198, 0.522]), emotional exhaustion EE ( r =−0.289, CI [−0.349, −0.227]), and depersonalisation DP ( r =−0.281, CI [−0.340 to −0.221]). Teachers’ constructivist beliefs ( r =0.159, CI [0.010, 0.302]) and teacher stress ( r =-0.134, CI [-0.169, -0.098]) were similarly correlated to a small degree.

Classroom-level Factors

Table 3 presents the overall effects of the association between various classroom-level factors and teachers’ CMSE. Our results showed that six classroom-level factors were significantly related to CMSE. There was no significant effect size associated with size of class. Among these six factors showing relationships with significant effect sizes, classroom climate ( r =0.552, CI [0.210, 0.774]) and classroom management practice ( r =0.436, CI [0.108, 0.679]) showed large and positive correlations with CMSE. Moderate correlations were observed for students’ misbehaviours ( r =-0.297, CI [-0.395, -0.192]) and student achievement ( r =0.382, CI [0.307, 0.453]). In terms of classroom interaction, there were four studies included in this meta-analysis. Our results show that all dimensions of classroom interactions were significantly related with teachers’ CMSE with a medium-level effect. For student-teacher relationship, we found that conflict ( r =-0.381, CI [-0.636, -0.050]) negatively related to teachers’ CMSE. Conversely, closeness had no relationship with teacher’ CMSE.

School-level factors

Table 4 presents the overall effects of the association between various school-level characteristics and teachers’ CMSE. This category presents new information about associations among the identified factors and CMSE and contains five distinct factors: principal gender, principal leadership, school type, school size, and school culture. Of these factors we found that only principal leadership and school culture were positively related to teachers’ CMSE, both with a low-level effect.

Moderator analysis

We assessed the heterogeneity of the results using the Q statistic and the I 2 index (see Tables 2 , 3 , 4 ). The Q tests yielded statistically significant results for a total of 11 factors: teacher gender, teacher working experience, job satisfaction, teacher commitment, teacher burnout, classroom climate, classroom management practice, students’ misbehaviour, student-teacher relationship, principal leadership, and school type, which may be influenced by moderators. Of these 11 factors, only seven factors were included in moderator analysis, as fewer than five studies were available for the remaining factors.

Teacher gender

Meta regression suggested that publication year (β=0.006, P=0.613) did not moderate the relationship between teacher gender and their CMSE. Subgroup analyses suggested effects were no different across publication types (Q bet =1.189, P=0.276), or school levels (Q bet =5.628, P=0.344). However, subgroup analysis showed that effects varied across countries (Q bet =53.543, P <0.001).

Teacher working experience

Meta regression suggested that publication year (β=-0.000, P=0.986) did not moderate the relationship between teacher working experience and CMSE. Subgroup analysis suggested publication type did not significantly relate to the correlation outcomes (Q bet =0.063, P=0.802). However, subgroup analyses show that effects varied across countries (Q bet =11850.865, P <0.001), and school levels (Q bet =351.484, P <0.001).

Job satisfaction

Meta regression and subgroup analysis suggested that publication year (β=-0.013, P=0.065) and publication type (Q bet =0.006, P=0.939) did not moderate the relationship between teacher job satisfaction and CMSE. However, other subgroup analyses show that countries (Q bet =11.359, P=0.045) and school level (Q bet =14.649, P=0.002) significantly moderate the relationship between job satisfaction and CMSE.

Teacher burnout

Meta regression suggested that publication year (EE: β=0.005, P=0.308; DP: β=0.008, P=0.091; PA: β=-0.006, P=0.543) did not moderate the relationship between teacher burnout and CMSE. Subgroup analyses suggested that there were no differences in effect across publication types (EE: Q bet =4.690, P=0.030; DP: Q bet =3.530, P=0.060; PA: Q bet =0.805, P=0.370) or school levels (EE: Q bet =2.495, P=0.476; DP: Q bet =2.759, P=0.430; PA: Q bet =4.661, P=0.198). Country, however, emerged as a significant moderator of the relationships between teacher burnout and CMSE (EE: Q bet =27.700, P <0.001; DP: Q bet =25.687, P<0.001; PA: Q bet =44.212, P <0.001).

Classroom management

Meta regression subgroup analysis suggested that publication year (β=0.024, P=0.928) and publication type (Q bet =0.000, P=1.000) did not moderate the relationship between teachers' classroom management practice and their CMSE. However, other subgroup analyses showed that effects varied across countries (Q bet =1204.532, P <0.001), and school levels (Q bet =99.343, P <0.001).

Students’ misbehaviour

Meta regression and subgroup analysis suggested that publication year (β=0.009, P=0.541), publication type (Q bet =0.000, P=1.000) and school levels (Q bet =1.376, P=0.241) did not moderate the relationship between students’ misbehaviour and teachers’ CMSE. However, other subgroup analyses show that effects varied across countries (Q bet =9.245, P=0.026).

Principal leadership

Meta regression and subgroup analyses suggested that there were no differences in effect across publication year (β=-0.009, P=0.459), publication types (Q bet =0.192, P=0.662), or countries (Q bet =0.221, P=0.895). School level, however, emerged as a significant moderator of the relationship between principal leadership and teachers’ CMSE (Q bet =12.792, P=0.002).

Publication bias

In general, articles with positive or statistically significant results are more likely to be published, which can lead to publication bias (Rothstein, 2008 ). Therefore, the present study conducted Egger’s regression test (Egger et al., 1997 ) to assess publication bias (see Table 5 ). Given that less than three studies included teacher personality, constructivist beliefs, class size, students’ achievement, principal gender, school size, school type and school culture, we did not run publication bias procedures on these effect sizes. The results for the analysis of the factors included indicated that there was no publication bias in meta-analyses for most of the factors, with only studies related to teacher educational level showing such bias.

However, for teacher educational level, the trim and fill procedure (Duval & Tweedie, 2000 ) signalled no bias (0 trimmed studies). In addition, the Classic fail-safe N (Rosenthal, 1979 ) test was performed to check the robustness of this finding by computing the number of studies that would be required to nullify the effect. A larger value for this coefficient indicates that we can be confident on the effects, despite the presence of publication bias. Whereas, if the number of missing studies is relatively small then there is indeed cause of concern. The value of fail-safe N of this analysis is 9975, which means that we would need to include 9975 studies to nullify the observed effect. Put another way, publication bias did not pose a threat to the meta-analytic result for teacher educational level.

Discussion and implications

The primary objective of the systematic review was to identify and review the existing evidence regarding factors that correlate with CMSE. A total of 87 studies were included in the review and 22 correlates were identified from these included studies. The findings of the systematic review clustered the factors related to CMSE into three themes (teacher-level factors, classroom-level factors, and school-level factors). Given the number of studies that met inclusion criteria for each analysis ranged from 29 to 2, interpretation of effect sizes derived from the meta-analyses still requires caution.

Turning to the first major strand, we found teacher personal factors are thoroughly examined in CMSE research and 10 teacher-level factors related to CMSE were identified. As for teacher demographic characteristics, our results indicated that teacher gender, teacher age, and teacher educational level were not significantly related to CMSE, the only exception being teacher working experience, for which a positive association was found indicating that more experienced teacher are more likely to hold higher level of confidence about their classroom management ability. Previous research (Brouwers & Tomic, 2000 ; Calkins et al., 2021 ; Fackler et al., 2021 ; Hettinger et al., 2021 ; Hu et al., 2021 ; Klassen & Chiu, 2010 ; Lazarides et al., 2020 ; Tran, 2015 ; Valente et al., 2020 ; Zee et al., 2016 ) reported mixed resulted were found for the relationship between many teacher demographic variables and CMSE. While our synthesis results provide a definitive conclusion in response to the current mixed results, one should be wary as these are conclusions drawn from cross-sectional data, especially for time-related characteristics like teacher age and working experience.

Among these six psychological correlates of CMSE (teacher constructivist beliefs, teacher stress, job satisfaction, teacher commitment, teacher personality and teacher burnout), teacher burnout stood out with a large effect based on a substantial number of studies and large sample size. Job burnout is a psychological syndrome that develops when individuals are under prolonged stressful work conditions, including three dimensions of emotional exhaustion, depersonalization and diminished personal accomplishment (Maslach, 2003 ). Fernet et al. ( 2012 ) reported that many teachers have experienced job burnout. Our results suggest that there is a negative relationship between CMSE and the three dimensions of burnout (i.e., emotional exhaustion, depersonalization, and diminished personal accomplishment), of which the largest effect is between CMSE and diminished personal accomplishment. This is in line with previous meta-analysis conducted by Aloe et al. ( 2014 ). When CMSE increases, the teacher’s feelings of emotional exhaustion and depersonalization decrease, and feelings of personal accomplishment increase.

Our synthesis results also indicated that teacher commitment and job satisfaction had moderate and positive associations with CMSE, whereas teacher stress had a low and negative association with CSME. These findings were as expected since self-efficacy in classroom management serves as a personal resource and plays an important role in teachers’ stress development or management. Teacher commitment and job satisfaction achieved medium effect (r= 0.302), which indicated classroom management plays a significant role in teachers’ wellbeing. Classroom management has been cited as a significant factor contributing to teacher stress and one of the primary causes of teacher turnover (Aloe et al., 2014 ; Davis, 2018 ). A small effect for teacher stress might be explained by the fact that teacher stress encompasses multiple dimensions (e.g., workload stress and classroom stress) and subsequently, attention should be paid to the relationship between sub-dimensions of teacher stress and CMSE (Klassen & Chiu, 2010 ). Although emotional arousal has been viewed as one of the sources of TSE, it was also suggested that self-efficacy could have a dampening effect on psychological stress arousal (Bandura, 1997 ). The relationship between CMSE and teacher burnout/teacher stress seems clear, however, the directions of these relationship are still unknown. Longitudinal research is highly recommended to clarify the directions.

Lazarides et al. ( 2020 ) suggested that TSE functions as a part of teachers’ personal resources. Attention has been paid to the relationship between teacher personality traits and CMSE, which has been examined among pre-service teachers (Senler & Sungur-Vural, 2013 ; Yingjie & Yan, 2016 ) and in-service teachers (Bullock et al., 2015 ; Rahimi & Saberi, 2014 ). Across these two studies, openness showed the largest correlation among the Big Five personality traits, followed by extraversion and conscientiousness, whereas agreeableness and neuroticism had no relationship with teachers’ CMSE. This finding was partially in line with previous studies (Bullock et al., 2015 ; Rahimi & Saberi, 2014 ), where openness and extraversion were significantly correlated with CMSE, while mixed results were found for conscientiousness, agreeableness and neuroticism. People high in openness are receptive to new things, have a wide range of interests, are imaginative and creative (Xiaoqing, 2013 ), suggesting that teachers rating higher on this trait may have more opportunities to practice new classroom management approaches and be more likely to be persistent in stressful situations. Compared with introverted peers, extroverted teachers are more sociable and self-confident. They may have been more likely to be engaged in various activities (Reeve, 2009 ), more likely to discuss with other teachers about how to manage classroom (Bullock et al., 2015 ), and approach more opportunities to gain experience and improve their ability to promote classroom management self-efficacy. Conscientiousness refers to dependability and the ability to resist impulsive behavior. Teachers with strong conscientiousness often weaken their negative emotions and enhance their positive emotions in their work (Xiaoxian et al., 2014 ). It may appear reasonable to correlate neuroticism with teachers’ CMSE as teachers higher on this trait are more likely to experience anxiety and stress when facing disruptive classroom environment. However, we did not find a relationship between these two. Teachers who are more agreeable are more likely to be empathetic and more pleased to help others. However, we did not find correlation between agreeableness and CSME. This seems to be another indication of the complexity of classroom management, where teachers merely showing care and empathy towards students may not contribute to a well-managed classroom. Considering the limited studies included in this meta-analysis and mixed results found in previous research, we should be cautious about this definitive conclusion. Further research focusing on the relationship between teacher personality traits and CMSE is recommended.

Teachers’ constructivist beliefs about teaching have been viewed as an intrinsic teacher characteristic. Across two studies, our meta-analytic results indicated a small but positive association between teachers’ constructivist beliefs and CMSE. This finding was expected as teachers who hold higher level constructivist beliefs were demonstrated to hold higher level of global TSE (Fackler et al., 2021 ; Fackler & Malmberg, 2016 ). Teachers who hold a high level of constructivist beliefs prefer to use student-centred teaching methods, focus on facilitating students’ learning, and tend to believe they are capable of managing their classroom.

Our meta-analysis showed that almost all classroom characteristics were highly influential in teachers’ CMSE, except class size and closeness in teacher-student relationships. Our synthesised result indicated that CMSE did not relate to class size, however, previous studies (Fackler et al., 2021 ; Kunemund et al., 2020 ) found a significant but negative relationship. This can be explained by the limited number of included studies (only two) potentially leading to unstable findings.

In relation to teachers’ behaviours towards students, one of the most mentioned factors was teachers’ classroom management practice. Our results suggested that CMSE functioned as a personal resource and positively related to positive aspect of classroom management, which is in line with the theoretical assumption of Bandura’s ( 1997 ) self-efficacy theory, self-efficacy acts as a mediating factor between individual behaviour and knowledge. On the other hand, teachers’ appraisals of past performance (e.g., classroom management practice) have been viewed as one of the sources of self-efficacy, however, Morris et al. ( 2017 ) found that teachers reflect on a variety of sources when they reflect on past performance. Instead Morris et al. ( 2017 ) suggested to conceptualize mastery experiences as teachers’ desired goals, such as classroom climate, student achievement, high quality classroom interaction, positive teacher-student relationship.

Classroom climate refers to the instructional and social-emotional environments students live in, which showed a positive relationship with CMSE and had a large effect size. A good classroom climate means teachers have less focus on individual student behaviours, focusing instead on building a positive learning climate. Many studies also paid attention to classroom interaction, as classroom processes are identified as teacher-student interaction pattern that have a significant impact on students’ outcomes (Mashburn et al., 2008 ). The classroom interactions framework (Hamre et al., 2013 ) focuses on teachers’ classroom interactional behaviours in three domains: emotional support, classroom organization, and instructional support. Ryan et al. ( 2015 ) found that American elementary and middle school teachers with higher CMSE tend to exhibit better emotional, behavioural, and instructional support. These findings were also noted in a study on Chinese preschool teachers (Hu et al., 2021 ). Our synthesis results confirmed the moderate and positive relationship between classroom interaction and CMSE among 4 studies, which indicated that teachers who feel confident in their classroom management skills are more likely to provide higher quality emotional support, classroom organization, and instructional support to their students.

One of the most important goals of classroom management is to establish positive student-teacher relationships (Evertson & Weinstein, 2006 ). Our results showed that conflict within teacher-student relationships was negatively related to CMSE and had a medium effect size, while closeness did not show any relationship. Teachers experiencing higher degrees of conflict in their relationships with students are more likely to have stronger emotional vulnerability and result in an increased likelihood of perceived professional and personal failure (Spilt et al., 2011 ), thereby they are at higher risk of developing unhealthy self-efficacy beliefs in classroom management. However, positive aspects of student-teacher relationships (closeness) did not show a positive relationship with CMSE as expected. This seems to indicate that teachers perceived conflict with students would lead to teachers feeling less confident in classroom management, yet being close to students does not make teachers feel empowered in classroom management either. We still need to be cautious about this finding as only three studies were included in this meta-analysis and one previous study (Zee et al., 2017 ) found a significant and positive relationship between closeness and CMSE.

In terms of students’ outcomes, many studies found a positive relationship between teacher general self-efficacy and students’ achievement (Fackler & Malmberg, 2016 ; Malmberg et al., 2014 ), but the focus of the research was rarely on the area of classroom management. Across two studies, we found a moderate and positive relationship between student achievement and CMSE, which further highlights the importance of self-efficacy in classroom management. Response towards student misbehaviour is the key part of managing classrooms and our results showed that student misbehaviour had a moderate and negative association with CMSE, which indicated that teacher with higher level of CMSE are more likely to experience less problem behaviours in the classroom.

Compared to the teacher- and classroom-level characteristics, few school-level factors have been identified and few studies were included in each meta-analysis (range from 2 to 5). We found principal gender does not correlate with CMSE based on two studies, however, one (Fackler et al., 2021 ) found a negative association. Our synthesis results also indicated that principal leadership has a significant impact on teachers' perceptions of CMSE. This suggests that principals play an important role in teachers’ sense of confidence in classroom management however, further research on how principals are of influence is recommended.

School culture is deeply rooted in people's attitudes, values and skills (Sezgin, 2010 ). We found two studies focused on school culture (McLeod, 2012 ; Öztürk et al., 2021 ) and the synthesised result found a small but positive association with CSME. Given individual's sense of efficacy is influenced by their interaction with their environment (Bandura, 1997 ), this supports the connection between environmental conditions (like organizational culture) and CMSE. Our results indicated that teachers’ efficacy beliefs towards classroom management did not differ based on school type (i.e., private vs public) or school size, however, Fackler et al. ( 2021 ) found private and larger schools were positively associated with teachers’ CMSE. This may again be due to the limited number of included studies. Further investigation into the examination of the relationship between school-level factors and their CMSE seems worthwhile.

In addition to the overall effect, we conducted moderator analysis and the results showed that participants’ countries and school levels played a role in moderating the relationship between CMSE and many correlates (e.g., teacher working experience, classroom management, job satisfaction). This may suggest that future research could focus on the differences of CMSE at different teaching levels. Research conducted in different countries proved to be a moderating variable, which supports the call for additional cross-cultural/cross-national studies of TSE (Fackler et al., 2020 ; Vieluf et al., 2013 ).

Teacher self-efficacy towards classroom management is an important facet of teacher self-efficacy, and the mechanisms driving efficacy beliefs toward classroom management remain unclear as a result of inconsistent findings across studies. This meta-analytic review synthesizes the literature over the last two decades to identify factors that correlate with CMSE and to estimate the effect size of these relationships. The findings identified 22 correlates of CMSE and clustered these correlates into three categories: teacher-level factors, classroom-level factors, and school-level factors. Teacher personal factors are thoroughly examined in current CMSE research, while there seems to be a lack of attention on teacher-student interaction and teachers’ working conditions. We identified 10 teacher-level factors including teacher demographic characteristics and psychometric variables. All teacher demographic characteristics except teaching experience were not related to teachers’ CMSE, whereby a positive association was found between level of CMSE and years of teaching experience. Six psychological correlates of CMSE (teacher constructivist beliefs, teacher stress, job satisfaction, teacher commitment, teacher personality and teacher burnout) were identified and most of them showed medium to large correlations with CMSE. Seven classroom-level factors were identified (classroom climate, classroom management, students’ misbehaviour, students’ achievement, classroom interaction, and student-teacher relationship) and almost all factors were significantly related to CMSE, except class size and closeness in teacher-student relationships. Limited studies focused on the relationship between teachers’ working environment and CMSE. Five school-level factors were identified, and only principal leadership and school culture showed a small and positive relationship with CMSE. In addition, sub-group moderation analysis revealed most of these effect sizes differed as a function of participants’ countries and school levels. Future work should focus on exploring more classroom- and school-level factors and conducting cross-cultural comparison research in order to contribute to a comprehensive body of literature. Experimental and longitudinal studies should also be the focus of future CMSE research due to the large amount of correlation work currently contributing to the field. Likewise, reviews of TSE in other specific areas, such as student engagement, instructional strategies and/or inclusive education, are needed to help shed light on those areas where self-perceptions of TSE are more diversified or where it is a global trait.

Limitations

There were some limitations to this study. First, we were selective about the studies included in our meta-analysis. To include relevant correlates of CMSE as comprehensively as possible, correlates included in two or more studies were meta-analysed. The limited number of studies included in some analyses (e.g., school culture, school type, school size) may have an impact on the validity of the synthesis results. A potential second limitation is that we did not place any restriction on the measurement instrument for CMSE and any other psychometric factors. Although previous meta-analyses reported that effect sizes did not vary in studies with different scales (Madigan & Kim, 2021 ), it is still theoretically a potentially important variable. A third potential limitation was that we only provided the correlates of CMSE and we cannot assess the predictors and outcomes. We recommend conducting longitudinal and quasi-experimental research in the future to explore in more depth the directions of these relationships.

Data availability

Data is available on request.

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Duan, S., Bissaker, K. & Xu, Z. Correlates of teachers’ classroom management self-efficacy: A systematic review and meta-analysis. Educ Psychol Rev 36 , 43 (2024). https://doi.org/10.1007/s10648-024-09881-2

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**** General Psychology, Islamic Azad University, Science and Research Branch, Tehran, Iran

F Shahgholy Ghahfarokhi

***** Clinical Psychology, Islamic Azad University, Science and Research Branch Branch, Isfahan, Iran

Objective: Carrying out the appropriate psychological interventions to improve vitality and mental well-being is critical. The study was carried out to review the effectiveness of stress management training on the academic life and mental well-being of the students of Shahed University.

Methodology: The method used was quasi-experimental with a pretest-posttest plan and control group. Therefore, a total of 40 students of Shahed University of Tehran were selected by a convenience sampling method and were organized into two groups: experimental and control group. Both groups were pretested by using an academic vitality inventory and an 84-question psychological well-being inventory. Then, the experimental group received stress management skills training for ten sessions, and the control group did not receive any intervention. Next, both groups were post-tested, and the data were analyzed with SPSS-21 software by using descriptive and inferential statistical methods.

Findings: The findings showed that the stress management skills training significantly contributed to promoting the academic vitality and psychological well-being of students (p < 0.001).

Conclusions: It was concluded from this research that teaching the methods for dealing with stress was an effective strategy to help students exposed to high stress and pressure, and this was due to its high efficiency, especially when it was held in groups, had a small cost, and it was accepted by the individuals.

Introduction

Challenges during education create sources of stress for students, and put their health at risk, in a way that affects their learning abilities [ 1 ]. Therefore, paying attention to the factors that could have a positive impact on the agreeableness and could increase the positive psychological states, and as a result, the physical and psychological health of the students was of great importance.

Among the important factors that affect people’s ability to adapt to the stresses of studying era is academic vitality [ 2 ]. Academic vitality means an adaptive response to various challenges and barriers experienced during education [ 3 ]. When a person does things spontaneously, does not feel not only frustrated and tired, but also constantly feels the strength and increased energy, and overall has a sense of inner vitality [ 2 ]. Therefore, the academic life has a relationship with the individual’s adaptation to the various situations of the academic period, feelings of self-efficacy and empowerment in the face of challenges, experiencing less anxiety and depression, a sense of responsibility in dealing with the academic tasks and better academic success [ 3 ]. Despite the high importance of academic vitality in the successful confrontation with the challenging academic period, the literature review of the studies managed in Iran showed that few studies were performed on the factors promoting this important variable. Therefore, an attempt to address this research gap increased the need for the current study.

Another important positive psychological state in students is the psychological well-being. The psychological well-being factor is defined as a person’s real talents growth and has six components that are the purpose in life, positive relations with others, personal growth, self-acceptance, autonomy, and environmental mastery [ 4 ]. The purpose in life means having a purpose and direction in life and pursuing them [ 5 ]. Positive relations with the others mean having warm, satisfactory relations along with confidence and empathy [ 6 ]. Personal growth means having a sense of continuous growth and the capacity for it and having an increased sense of efficacy and wisdom [ 4 ]. Self-acceptance means having a positive attitude towards oneself and accepting the various aspects of oneself [ 6 ]. Autonomy means the feeling of self-determination, independence, and self-assessment against personal criteria [ 4 ]. Moreover, environmental mastery means a sense of competence and the ability to manage the complex environment around [ 5 ].

However, one of the most significant parts affecting the psychological health and well-being of individuals is life skills training [ 7 ]. Life skills’ training is critical for students, in a way that on this basis, many universities have started to teach life skills and stress management skills to improve the physical and psychological health of their students in the recent years [ 8 ]. The main objective of the World Health Organization regarding the creation of a life skills plan is in the field of psychological health. Therefore, different societies throughout the world try to promote the implementation and evaluation of the programs training in life skills. It focuses on the growth of mental abilities such as problem-solving, coping with emotions, self-awareness, social harmony, and stress management among children, teenagers, and even adults [ 9 ]. From the life skills, training in stress management skills is critical, because students need to deal effectively with stressful issues and factors. Accordingly, it was thought that teaching stress management skills is very efficient in improving the students’ positive psychological states, in particular, their vitality and mental well-being. Therefore, this study examined the effectiveness of the stress management skills training on the academic life and psychological well-being among Shahed University students.

Methodology

The study was quasi-experimental with a pretest-posttest. The analytical community of the study included all the students of Shahed University of Tehran in the fall of 2015, who were selected with a convenience method. For the calculation of the sample size, the appropriate sample size in experimental studies was of 15 people for each group [ 10 ]. At first, the sample size of 15 individuals was selected for each group. Then, to increase the statistical power and to manage the possible decrease in the number of participants, the sample size of 20 individuals (n = 20) was considered for each group. The sampling was voluntary non-random from among all the students studying at Shahed University. The inclusion criteria included an informed consent and the willingness to participate in the research, the ability to take part in the sessions and to collaborate in carrying out assignments, willingness to cooperate in completing the instruments, and the age range of 18 to 35 years. The exclusion criteria included the lack of desire to participate in the sessions and the absence to more than three courses in the preparation method, the lack of the ability to participate in the sessions, lack of cooperation in carrying out assignments, and receiving any training or psychological therapy that was not part of the program of this research.

The procedure of the study was that from all the students studying at Shahed University, a number was non-randomly and voluntarily selected, and if they met the inclusion criteria, they were randomly assigned to two groups: experimental and control. At the beginning and before starting the study, an informed consent was obtained from all of them to uphold moral considerations, through informing them of the aim of the study and the impact of such studies in improving their psychological status. Then, all the information of the participants were collected, and they were assured that the information would remain confidential by the researcher. Then, the experimental group received group stress management training for ten sessions, and the control group did not receive any intervention. In the end, both groups were post-tested. The protocol of stress management training sessions is presented in Table 1 .

Protocol of stress management skills training sessions

The instruments used in the study included a demographic sample page, an academic vitality questionnaire, and a psychological well-being scale (PWBS-18).

Demographic sample page: The demographic sample page included age, gender, educational level, and marital status. The sample page was prepared and evaluated by the researchers of the study.

Academic vitality questionnaire: This questionnaire was developed by Dehqanizadeh MH, Hosseinchari M (2012) [ 3 ], based on the academic vitality scale of Martin AJ, Marsh HW (2006) [ 15 ], which had four items. After various implementations of the items of the questionnaire, the final version was rewritten, and the result was that the revised version had ten items. Then the items above were again examined in a preliminary study on a sample including 186 high school students, who were chosen by using a cluster random sampling, and their psychometric properties were examined. The results of the examination showed that the obtained Cronbach’s alpha coefficient, by removing [ 3 ] item number 8, was 0.80 and the retest coefficient was 0.73. Also, the range of correlation of the elements with the total score was between 0.51 and 0.68. These results indicated that the items had a satisfactory internal consistency and stability.

Psychological well-being scale (SPWB): Riffe’s mental well-being scale [ 11 ] was made up of 84 questions in Likert’s 7-degree scale (from “strongly disagree” to “agree strongly”). It was a self-report questionnaire, which measured six components of the psychological well-being, including purpose in life, positive relations with others, personal growth, self-acceptance, autonomy, and environmental mastery. The internal consistency coefficients for the components of this questionnaire were obtained from 0.83 to 0.91. In Mohammadpour and Joshanloo research (2014) [ 6 ], the reliability coefficient of this scale with Cronbach’s alpha method for the psychological well-being scale obtained was 0.81. Also, for the subscales of the test including self-compliance, environmental mastery, personal growth and development, link with others, the goal in life, and self-acceptance were obtained at 0.60, 0.64, 0.54, 0.58, 0.65, and 0.61, respectively. A study performed by Kafka and Kozma (2002) was conducted to verify the validity of the items of the Riffe’s psychological well-being scale. The findings showed that there was a high correlation between this scale and the subjective well-being scale (SWB) and the satisfaction with life scale (SWLS). In the present study, the reliability coefficient with Cronbach’s alpha method for the psychological well-being scale obtained was 0.81. Also, for the subscales of the test, including self-compliance, environmental mastery, personal growth and development, relations with others, the goal in life, and self-acceptance were obtained at 0.60, 0.64, 0.54, 0.58, 0.65, and 0.61, respectively.

The SPSS-20 software was used for data analysis. The statistical method used for the data analysis of the research on the level of descriptive statistics was mean, standard deviation, frequency, and frequency percentage indexes, and on the inferential statistics, univariate and multivariate analysis of covariance model were used.

Findings of the research

The demographic properties of the sample present in the study are presented in Table 2 .

Demographic characteristics of the subjects

As presented in Table 1 , the largest frequency of participation belonged to the participants in the age range of 21 to 25 with 14 individuals (35%) and the lowest frequency of individuals in the range of 18 to 20 years, with six individuals (15%). In addition, the mean age of the participants was 24.85, and the standard deviation was 4.41. The other information about the demographic properties of the present sample is provided in Table 2

As shown in Table 3 , the mean scores of purpose in life, positive relations with others, personal growth, self-acceptance, autonomy, environmental mastery, total score of psychological well-being, and academic vitality of posttest were increased in the test group as associated with the control group.

Descriptive stats of academic vitality and psychological well-being scores of the two groups divided by the pretest and posttest

As shown in Table 4 , the null hypothesis of the equality of variances of the two groups in the academic vitality and psychological well-being with all its components was confirmed. It meant that the variances of the two clusters in the population were equal and had no significant difference for the academic vitality and the psychological well-being variable with all its components. Thus, given the compliance with the Levene assumption, the analysis of covariance of the results of the hypothesis of the research were permitted.

Results of Levene test for the examination of the consistency of variances of academic vitality and psychological well-being variables with its components in the posttest stage

As shown in Table 5 , the significance level of all the tests (p < 0.001) indicated that there was a significant difference between the two groups at least in one of the dependent variables (academic vitality and psychological well-being with its components). And, according to the eta square, 0.89 percent of the differences observed among individuals were associated with the effect of the independent variable, which was the intervention method (stress management skills training). On the other hand, given that the statistical power was 0.95, which was higher than 0.80, the sample size was acceptable for the research. The results related to significant differences in any of the dependent variables are listed below.

Results of multivariate analysis of covariance on the scores of posttest with the control of pretest in the academic vitality and psychological well-being variable with its components

According to Table 6 , the significance level was p < 0.001, the hypothesis of the difference between the academic vitality and the psychological well-being with its components in the two groups was confirmed. It stated that 0.54, 0.25, 0.52, 0.64, 0.60, 0.59, 0.45 and 0.81 percent change in the academic vitality, individuals’ purpose in life, positive relations with others, personal growth, self-acceptance, autonomy, environmental mastery, and psychological well-being scores were due to the independent variable (stress management skills training). Therefore, it could be said that stress management skills training increased the academic vitality and the psychological well-being and all of its components.

The results of multivariate analysis of covariance to assess the impact of stress management skills training on the level of psychological well-being and its components in the posttest stage

Discussion and conclusions

Given the aim of this study, which was to examine the effectiveness of stress management skills training on the academic vitality and psychological well-being of the students of Shahed University, the results of the univariate and multivariate analysis of covariance showed that stress management skills training had a significant impact on increasing the academic vitality and psychological well-being. The findings indicated that the stress management skills training had a major impact on increasing the academic life. It was consistent with different studies of Habibi M (2015), Pakdaman A, Ganji K, Ahmadzadeh M (2012), Shirbim Z, Sudani M, Shafi-Abadi A (2008) [ 12 - 14 ].

In explaining their similar finding, Pakdaman A, Ganji K, Ahmadzadeh M (2012) [ 13 ] also stated that life skills training helped in the improvement of the academic conditions of the subjects. In addition, this was because of this training, with growing different skills of the students, helping the students know their strengths and weaknesses, and overall, help the individuals move from weaknesses and skill deficits to capable and strong skills. Therefore, this could provide the students with better educational conditions [ 14 ]. In explaining their similar finding, Shafi-Abadi (2008) stated that teaching life skills, including stress management skills, are one of the ways to improve the mental health of the individuals of the community and to prevent harms. In fact, these teachings protected the health and mental hygiene of the society and protected it against diseases, disabilities, and disturbances in human relations. As a result, the feeling of security and solidarity increased among the members of the society, and then their senses of happiness, vitality, and health increased.

The findings showed that stress management skills’ training has a significant impact on the psychological well-being. It was consistent with the multiple studies of Qadiri-Bahramabadi F, Mikaeli-Manee F (2015), Qanbari N, Habibi M, Shams-Aldini S (2013), Alavi-Arjmand N, Kashaninia Z, Hosseini MA, Reza-Soltani P (2012), Chubforushzadeh A, Kalantari M, Molavi H (2009) [ 16 - 19 ].

In explaining their similar findings, Qadiri-Bahramabadi F, Mikaeli-Manee F (2015) [ 16 ] stated that facing numerous stresses required teaching and learning of appropriate stress management skills. In other words, during stress, individuals must know the necessary coping skills to reduce the effects of stress, and if the pressure was managed and the effective coping skills were applied, the person would be able to get along better with the needs and challenges of his/ her life. Therefore, the intervention of stress management led to the formation of good feelings about oneself, as well as a positive performance in the stable world. It created interest and motivation in people’s lives as well as increasing the self-confidence of the individuals. As a result, it increased the psychological well-being.

In explaining their similar finding, Qanbari N, Habibi M, Shams-Aldini S (2013) [ 17 ] stated that with the help of multiple strategies to manage stress such as relaxation, and muscular relaxation, stress and anxiety could be reduced. The individuals identified the somatic symptoms, and with mastering the ways to acquire relaxation, which was inconsistent with stress, reduced their anxiety and unpleasant feelings, thus increasing the psychological well-being. Also, in explaining their similar finding, Chubforushzadeh A, Kalantari M, Molavi H (2009) [ 19 ], stated that stress management treatments make multiple changes in the individual’s beliefs, feelings, and behaviors. Therefore, improving the individual’s evaluations and coping skills, and the provided practices to integrate the learned separations with real life situations could lead to a decrease in the perceived stress and an increase in the psychological well-being.

Acknowledgement

The authors would like to thank the venerable authorities of Shahed University of Tehran for their assistance. Also, the authors would like to thank all the participants in the study.