Which has an impact on which? Academic performance or mental health. A nationwide empirical study of 50 universities in China

Which has an impact on which? Academic performance or mental health. 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Academic performance or mental health. A nationwide empirical study of 50 universities in China Haibao Zhu, JunHua Zhang, Tingzhong Yang, Sihui Peng, Joan L Bottorff This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5508756/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background While numerous studies have identified a strong association between academic performance and mental health, the direction of causality remains unclear. This study aims to examine whether academic performance impacts mental health or vice versa. Methods A multistage sampling survey was conducted among 11,659 medical students from 50 universities across the country. Two mediation models were constructed for analysis: (1) Academic stress → Academic performance → Mental health (AAM), and (2) Academic stress → Mental health → Academic performance (AMA). Data were analyzed using descriptive statistics and mediation analysis. Results The Bootstrap test showed that the 95% confidence interval (CI) ranged from 0.000 to 0.002 in AAM model and 0.003 to 0.008 in AMA model. Academic performance does not have a mediating effect in AAM, whereas in the AMA, mental health serves as a mediator. Although both the AAM and AMA models showed mediating effects by using the three-parameter comparison method, the ratio of the mediating effect to the total effect was 1.6% in the AAM model and 17.7% in the AMA model. Model fit indices in SEM revealed that, aside from the goodness-of-fit index (GFI), the AAM model did not meet acceptable fit criteria. In contrast, the AMA model showed good fit across most indices. Conclusion The findings support the AMA model, suggesting a more plausible causal pathway in which mental health significantly affects academic performance. This insight underscores the importance of addressing mental health concerns to enhance students’ academic outcomes, offering valuable guidance for educational policy and student support interventions. Mental Health Academic Performance Medical students Occupational stress Structural Equation Modeling Background Academic performance refers to students’ mastery of course content and skills, their ability to complete academic tasks, and their overall academic achievement. It is linked to individual development and well-being [ 1 ] and impacts not only current students’ academic successes at school but also their future career and work opportunities[ 2 ]. In education and health fields the association between academic performance and mental health has garnered increasing attention from researchers, policymakers, and practitioners. Many researchers suggest that there are close associations between academic performance and mental health, but the causal direction of the relationship between the two is inconsistent [ 3 – 6 ]. Many researchers have found that adolescent mental health problems not only cause pain and distress but also negatively affect their academic success [ 7 – 11 ]. On contrary, some studies have shown that poor academic performance can lead to mental health problems [ 1 , 12 , 13 ]. The relationship between mental health and academic performance is intricate and multifaceted, with numerous factors influencing its dynamics. It is important to provide a nuanced understanding of the mechanisms underlying the link between mental health and academic success [ 5 ]. However, traditional education and health paradigms have often treated mental health and academic success as distinct domains. Understanding mechanisms of this relationship is essential for educational and health policy, practice, and intervention strategies aimed at promoting student well-being and academic success. Learning activities are cognitive processes. Research has shown that the impact of mental health on academic performance is mediated through cognitive deficits related to mental health problems. Some researchers have demonstrated that mental health influences students' motivation, self-efficacy, and self-regulation, which in turn have an impact on academic performance[ 4 , 14 ]. Others have pointed to the significance of affective states, such as anxiety and affect, in influencing motivation and self-regulation, highlighting their impact on cognitive processes and academic performance [ 5 , 15 ]. Furthermore, mental health influences health behaviors such as sleep, nutrition, and physical activity, which are crucial for cognitive functioning and academic performance [ 5 , 16 ]. The exploration of a mechanism mediated by a third variable in the above is helpful for understanding the causality. Building on these existing frameworks, the theory of mental stress offers another perspective on this relationship. In this context, academic stress, introduces as the third variable to explore the possible causal pathway between academic performance and mental health. According to the Stimulus-Response-Mental and Behavior Problem Theory (SRM), any stimulus can make people aware of the serious threat it poses and elicit a mental response, ultimately leading to mental and behavioral problems [ 17 , 18 ]. In this study, academic stress is as the stimulus, while the behavioral and mental problems refer to academic achievement and mental disorders. Based on tis theoretical hypotheses, this study proposes two potential causal models: (1) Academic stress → Academic performance→ Mental health (AAM) and (2) Academic stress→ Mental health → Academic performance (AMA). As for AAM model, major causes of stress among students reported in the literature include high academic workload, lack of learning materials/resources, poor performance in examinations and difficulty reading and understanding modules. Adu (2023) reported that academic stress can have a negative impact on students' academic performance and health[ 19 ]. Furthermore, Cao (2023) found that academic anxiety partially mediates the relationship between academic stress and academic burnout[ 20 ]. Consequently, academic performance may contribute to mental health problems[ 1 , 12 , 13 ]. There is also evidence to support the potential possibility of AMA model [ 21 , 22 ]. The correlation between high levels of academic stress and mental health or psychological well-being has been demonstrated in a number of studies [ 19 , 23 , 24 ]. Moreover, mental health problems have been shown to have a negative impact on academic performance [ 7 – 11 ]. This study will explore the possibility of the existence of the two models mentioned above. The causal relationship between academic performance and mental health has long puzzled researchers in the academic community. Clarifying this issue holds significant implications for both academic research and practical applications in education and health policy. Research Methods Study Design Subjects This study employed a multistage sampling design.In the first stage, 60 universities with medical programs were selected as potential study sites as part of the Bloomberg Global Tobacco Control Advocacy Capacity Building Project[ 25 ]. These universities are located in 42 cities with different geographical regions of China mainland. 50 universities completed baseline surveys and passed the project evaluation (e.g., program availability, administrative cooperation, data completeness), including 22 medical schools and 28 comprehensive universities with medical programs. In the second stage, sample classes, which taking medical courses, within each university were identified. In the third stage, every student in these classes was designated as a study subject.A more detailaed description of the survey and the data can be found in Yang et al.(2015)[ 25 ]. Data Collection Data were collected using anonymous questionnaires. The questionnaire used in this study has previously been developed and named Global Health Professions Student Survey (GHPSS)[ 25 ]. Responses were gathered in classrooms and took approximately 30 minutes to complete. This study was approved by the Ethics Committee of the Medical Center of Zhejiang University, and verbal consent was obtained from all participants. Measurement Academic performance To achieve stable measurement results, academic performance was measured using a relative method[ 6 ]. Participants were asked, "What is your current academic performance position in your class?", with responses ranging from "top third," "middle third," to "bottom third," assigned values of 1, 2, and 3, respectively. Mental health Mental health was assessed using the Chinese version of the Perceived Stress Scale (CPSS), which measures perceived stress levels over the past month [ 26 ]. This scale comprised 14 items that addressed perceptions of stress during the month prior to the survey. Items were rated on a 5-point Likert-type scale that ranged from 0 (never) to 4 (very often). Item scores were summed to yield a total stress score (Mental health), with higher scores indicating high risk of mental disorders. This scale has been widely used to assess stress in China and has been shown to be an appropriate indicator of mental health status [ 27 – 29 ]. Academic stress Academic stress in this study focus on medical professional concerns rather than strictly academic stress. Previous studies have reported that the acaedmic stress of current medical students is not only from teaching and learning-related stressors, but the concerns about the doctor-patien relationship[ 30 , 31 ] and context related to professionalism[ 32 , 33 ]. It was reflected by participants' perceived stress related to the medical profession and the current state of medical education, closely related to learning motivation[ 18 , 34 ]. These were measured through five questions. Participants were asked to indicate their perceved stress in following situations (1)The medical curriculum is extensive, and the learning tasks are heavy; (2)The learning content is tedious, and the teaching methods are monotonous; (3)Doctor-patient relationships are tense, making it difficult to be a doctor; (4)The work stress on doctors is high; (5)Studying medicine takes a long time and is costly. Responses were on a linked scale from "strongly disagree" to "strongly agree," assigned values of 1 to 5. Item scores were summed to yield a total academic stress score. The Cronbach’s alpha is 0.803 in this study, indicating adequate reliability. Control Variables Demographic variables included age, gender, ethnicity, family location, and major. Data Analysis All data were entered into a Microsoft Excel database and imported into SAS (version 9.4) for statistical analysis. Descriptive analyses were first conducted on sample distribution and variable attributes. To test the AAM and AMA models, this study constructed a mediation analysis[ 35 ]. The mediation analysis was conducted using the Bootstrap test and the three-parameter comparison method. The significance of the mediation effect was determined based on the following criteria: (1) indirect effect ( a × b) was tested using bootstrap resamples, the 95% confidence interval (CI) excluding zero indicated significance. (2) Path coefficients ( a , b , and c′ ) were tested ( p < 0.05). Full mediation was indicated if c′ was non-significant, while partial mediation was identified if c′ remained significant but reduced[ 36 ]. On this basis, SEM evaluation is based on the fit indices for the test of a single path coefficient and the overall model fit. Generally, the more fit indices applied to an SEM, the more likely that a miss-specified model will be rejected—suggesting an increase in the probability of good models being rejected. Several criteria were used to evaluate the model fit including (1) χ 2 /df less than 3; (2) goodness-of-fit index (GFI), comparative fit index (CFI), normed fit index (NFI) and non-normed fit index (NNFI) greater than 0.9; (3) root mean square error of approximation (RMSEA) less than 0.05; (4)root mean square residual (RMR) less than 0.01. Note that due to the large sample size, standard errors were small, leading to large χ 2 values and small p-values. Therefore, this index was only used as a reference [ 25 ]. The analysis involves centralizing all variables, and the analysis will be conducted using the relevant procedures in SAS (9.4). All analyses were conducted with the university as the grouping unit to control for cluster effects. All analyses included weighting. Weights consisted of (1) sampling weight, the inverse of the sampling probability by school, and (2) post-stratification weight, adjusted according to the estimated gender distribution in the national survey[ 25 ]. The final weight was the product of these two weights. Nonresponse weight was not used due to the very low nonresponse rate. Results The completion rate of valid questionnaires in this study was 97.8%, with a sample comprising 11,659 students from 50 different universities. Among the sample, 20.2% were under 19 years old, 18.8% were 22 years old or above, and the majority were between 20 and 21 years old. Males constituted 33.2% of the sample, while females made up 66.8%. Most of the respondents (72.2%) were sophomores and juniors. The majority of the sample (86.0%) were Han Chinese (see Table 1 ). Table 1 Demographic Composition of the Sample (N = 11,659) Group Number % Age (years) < 19 1,873 20.2 19–20 2,523 20.3 20–21 2,993 22.5 21–22 2,370 20.3 22 and above 1,900 18.8 Sex Male 3,722 33.2 Female 7,937 66.8 Grade 1st Year 968 9.5 2nd Year 4,255 35.7 3rd Year 4,455 36.5 4th Year and above 1,981 18.3 Ethnicity Han 10,713 86.0 Minority 946 14.0 Family Location Rural 5,079 51.3 Town 1,370 11.5 County 2,002 15.8 City 2,578 21.4 Major Preventive Medicine 2,589 24.6 Clinical Medicine 6,402 59.6 Nursing 1,061 6.4 Others 1,601 9.2 Table 2 showed the descriptive statistics of the variables used in the models. Table 3 presents the results of mediation analyses. In the AAM model, the Bootstrap 95% CI (0.000 ~ 0.002) included zero, indicating that academic performance does not significantly mediate the relationship. However, the comparison of the three key parameters—a, b, and c’—revealed that the direct effects of a*b and c’ were statistically significant and consistent in coefficient value of c. This suggests a partial mediating effect for academic performance. In this model, the mediating effect of academic performance is only 1.6%, indicating that its influence on the relationship between academic stress and mental health is minimal. In contrast, AMA model passed both Bootstrap and three-parameter comparison method test. Since the 95% Bootstrap CI (0.003 ~ 0.008) did not include zero, this confirms a significant mediating effect of mental health. Moreover, the statistical significance of parameters, a, b, and c’, the direct effects of a*b and c’ are the same with c, which indicates that there is a mediating effect. In this model, the ratio of the mediating effect to the total effect was 17.7%, indicating that mental health plays a much larger role in mediating the relationship between academic stress and academic performance. Table 2 Statistical value of variables in models Variable Mean SD Range Neg 17.40 3.52 0.000–12.000 Mental stress 2.02 0.49 0.111–4.111 Q12per 1.87 0.72 1.000–3.000 CHQ 2.59 1.72 0.000–12.000 Table 3 Analysis results of mediation effect in AAM and AMA model Model Total effect value(C) a b Mediation effect value (a*b ) a*b (95% Boot CI) Direct effect value (C’) AAM model 0.063** 0.006** 0.100** 0.001 0.000 ~ 0.002 0.062** AMA model 0.006** 0.062** 0.018** 0.001 0.003 ~ 0.008 0.005** **P < 0.01; *P < 0.05 Table 4 showed that summary of model fit statistics for structural models. In the AAM model, the resulting model revealed: χ 2 /df:188.400, GFI: 0.984, RMSEA: 0.127, RMR: 0.312, CFI: 0.150, NFI: 0.157, NNFI:0.550. With the exception of GFI, all fit indices failed to meet the criteria for a good model fit, suggesting that the AAM model does not provide an adequate fit to the data. But in the AMA model, the results were different: χ 2 /df: 7.32, GFI: 0.984, RMSEA: 0.999, RMR: 0.032, CFI: 0.971, NFI: 0.967, NNFI: 0.914. Table 4 That results of goodness-of-fit evaluation in AAM and AMA model. Model χ 2 / df GFI RMSEA RMR CFI NFI NNFI Ref 0.9 < 0.1 0.9 > 0.9 > 0.9 AAM model 188.400 0.984 0.127 0.312 0.150 0.157 -0.550 AMA model 7.321 0.984 0.999 0.023 0.971 0.967 0.914 Discussion Medical education is the education aimed at cultivating professionals in the field of medicine. Academic performance and mental health problems have always been of concern to researchers, decision-makers, and practitioners in the health and education fields. The causal relationship between them has long puzzled scholars. Clarifying this issue is of great significance both academically and in practice [ 3 , 5 ]. This study examined the possibility of the existence of models by comparing and analyzing the mediating effects in AAM and AMA model. The bootstrap test showed no mediating effect in the AAM model, whereas a significant mediation effect was found in the AMA model. This implies that the AAM model is not valid, while the AMA model is valid. Furthermore, the ratio of the mediating effect to the total effect was 1.6% in AAM model and 17.7% in AMA model and, indicating a much higher proportion in the latter compared to the former. Mental health plays a much larger role in mediating the relationship between academic stress and academic performance. AAM model did not meet the criteria for a good model fit. However, in AMA model, most indicators met the criteria for a good fit, indicating that the model is in a good fit state. The finding supports the validity of the AMA model, aligning with our hypothesis on the causal path from mental health to academic performance. The successful establishment of the AMA model suggests a plausible causal pathway in which mental health problems lead to poorer academic outcomes. This finding addresses a longstanding debate regarding the causal relationship between academic performance and mental health [ 3 – 6 ]. This association can be explained by the SRM Theory, which offers a window into the possible mechanisms that may link overuse of academic stress and poor academic performance though mental health problems[ 17 , 18 ]. This result is also supported by several prior empirical studies [ 1 , 7 , 10 , 13 ]. While this study introduced academic stress and mental health as predictors of academic performance, it is important to acknowledge that these constructs are also influenced by a broader range of individual, environmental, and contextual factors that were beyond this study’s scope. The AAM and AMA models were developed to examine the directional relationships among academic stress, mental health, and academic performance, rather than to quantify relative predictive values of different contributors. Nonetheless, these findings highlight the importance of addressing learning challenges from a mental health perspective as a strategy to support academic success. Given this, universities need to provide adequate and accessible mental health services for students. In Brazil, for example, initiatives such as the Psychosocial Care Space (Epsico) at the Universidade do Estado do Amazonas (UEA) and the Psychological Assistance Project at the Pontifícia Universidade Católica de Minas Gerais (APP/PUC Minas) exemplify institutional responses driven by administrative recognition of student mental distress, leading to the implementation of structured mental health support strategies [ 37 ]. Supporting this perspective, Phillips et al. (2022) found that more than one-third of medical students who perceived a need for mental health support did not receive any services. Their findings emphasize the importance of targeted outreach, particularly for underserved student populations, and highlight how reducing structural barriers, such as rigid academic schedules, are able to significantly enhance help-seeking behaviors and overall student well-being [ 38 ]. University environments that promote the mental well-being are also important[ 39 , 40 ]. This could include initiatives that have been shown to shift learning environments towards supporting student mental wellbeing such as building green environments, providing a range of extracurricular activities for students, strengthening interactions between teachers and students, as well as among students, and arranging study schedules to ensure there is enough rest time. Special university campus-based clinics should be established to provide mental health counseling to high-risk individuals. Offering stress management programs and providing mental health services where students can share their mental health concerns, engage in discussions with others facing problems, and learn stress management skills are also important initiatives for improving student mental health and building a positive campus community. This study shows that the β value for the relationship between academic stress and mental health is 0.062, while it is 0.005 for academic stress and academic performance. This suggests that academic stress has a greater impact on student mental health than academic performance. This is consistent with other studies that show high levels of academic stress have an impact on mental health[ 2 , 19 , 23 ]. This study further confirms the importance of addressing mental health problems among medical students from another perspective. Currently, medical students in China are faced with a heavy workload of courses, many of which are dull and taught using a single, uninteresting method. The intense academic stress on medical students has led to prominent mental health problems [ 6 ]. Efforts should be made to streamline and optimize medical courses. Courses should focus on core concepts, basic theories, and fundamental skills. There should be mechanisms in place to control the expansion of course content. Additionally, teaching methods should be innovated with the advent of the artificial intelligence era. Artificial intelligence technology can assist teachers in personalizing instruction, making teaching methods more flexible and efficient, thereby enhancing students' learning outcomes and experiences. In related development, Chen et al. (2023) designed and implemented a chatbot teaching assistant that effectively addressed students’ learning needs. The chatbot led to significant improvements in student performance on tasks requiring basic memory retention and conceptual understanding [ 41 ]. Fatima et al. (2023), in their scoping review, emphasized that the integration of AI in education has accelerated the use of advanced AI-driven methods, deep learning and virtual reality as examples, in medical training. They found that AI-based approaches can substantially enhance the practical skills of medical students [ 42 ]. AI shows great promise in enhancing teaching and learning, its implementation must be approached with careful consideration of ethical issues, privacy protection, data security, and equity to ensure responsible and inclusive adoption[ 41 – 43 ]. It should be noted that it is also possible to present the opposite direction from academic achievement to academic stress. For example, poor academic performance may lead to a sense of frustration with learning, thereby increasing academic stress. We must clarify that in this study, academic stress and academic performance are completely different concepts. The former refers to participants' perceived stress related to the medical profession and the current state of medical education, such as feeling burdened by a heavy academic workload, lacking interest in their field, and finding the content monotonous and unappealing. The latter refers to students' mastery of course content and skills, their ability to complete academic tasks, and their overall academic achievement. According to SRM Theory, academic stress is the stimulus, and academic achievement is the behavioral outcome. The logical model of AMA, established in SRM hypothesis, has already been validated. Furthermore, we conducted a mediation analysis and performed a fit examination for the reverse AAM pathway (mental health → academic performance → academic stress, RMAA) and the AMA pathway (academic performance → mental health → academic stress, RAMA). In RMAA, the results revealed a mediating effect of -0.031, with a Bootstrap 95% CI of (-0.008, 0.001), indicating that academic performance does not significantly mediate the relationship. The overall fit of the RMAA and RAMA models is poorer than that of the AMA model. This suggests that the overall fit of the AMA model is better, indicating that the model we hypothesized in this study is appropriate. According to the above results, the pathway from academic performance to academic stress should represent a feedback effect of behavioral outcomes, rather than a mainstream effect. However, the conclusions drawn here are based solely on theoretical and statistical analysis, and we cannot definitively determine solo causal links between these associations. There remains a need for further longitudinal, in-depth, and comprehensive exploration of the issues related to academic stress and its influence on academic performance and mental health. The study has some limitations. While this research is theoretically grounded and supported by empirical evidences, the methodological limitation lies in the fact that it is a cross-sectional study design, which cannot fundamentally establish causal links between academic performance and mental health. Further research should involve a complex design that combines quantitative and qualitative methods, with longitudinal observations. This is expected to provide further clarification on causal relationships. A second study limitation is that AMA model in this study may not be the only model that is useful for testing the causal relationship between academic performance and mental health. There may be other models’ worth further exploration. Another limitation of this study is that academic performance was self-reported by participants. Although self-reported academic performance is widely used in empirical research and has shown reasonable reliability[ 6 , 44 ]. However, it may be prone to biases such as social desirability and memory errors. Future research should consider incorporating objective indicator, such as the cumulative grade point average (GPA) over the course of students’ studies to enhance accuracy. Conclusion This study provides new and direct evidence that mental health has stronger impact on academic performance than the reverse. This directional relationship highlights the foundational role of mental health in supporting students’ academic success. Therefore, educational policies and curricula should prioritize mental health support as a central component, potentially leading to both improved well-being and enhanced academic outcomes. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee at the Zhejiang University Medical Center (Approval number: 2014:1-017). Verbal informed consent was obtained from all participants before data collection. Clinical trial number: not applicable. Consent for publication: Not applicable. This study does not include identifiable participant data. Declaration of competing interest The authors declare that they have no competing interests. Acknowledgements We thank local teams from the “Facilitate MOH endorsement of tobacco control implementation through promoting tobacco control advocacy capacity in medical schools “project (supported by UNION)” for organizing the data collection. The project universities, local government, and CDC also partly funded this survey. Funding This study was supported, in part, by Global Bridges/IGLC, 2014SC1(13498319) Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Pascoe MC, Hetrick SE, Parker AG: The impact of stress on students in secondary school and higher education. International Journal of Adolescence and Youth 2020, 25(1):104-112. 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Eley DS, and Slavin SJ: Medical student mental health – the intransigent global dilemma: Contributors and potential solutions. Medical teacher 2024, 46(2):156-161. Chen Y, Jensen S, Albert LJ, Gupta S, Lee T: Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success. Information Systems Frontiers 2023, 25(1):161-182. Nagi F, Salih R, Alzubaidi M, Shah H, Alam T, Shah Z, Househ MJHTwI, Intelligence A: Applications of artificial intelligence (AI) in medical education: a scoping review. 2023:648-651. Alam A: Harnessing the Power of AI to Create Intelligent Tutoring Systems for Enhanced Classroom Experience and Improved Learning Outcomes. In: Intelligent Communication Technologies and Virtual Mobile Networks: 2023// 2023; Singapore : Springer Nature Singapore; 2023: 571-591. Wu D, and Yang T: Late bedtime, uncertainty stress among Chinese college students: impact on academic performance and self-rated health. Psychology, Health & Medicine 2023, 28(10):2915-2926. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 May, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 29 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5508756","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440738680,"identity":"b68d69ce-3c48-4e11-94ec-fd66f4b6eff9","order_by":0,"name":"Haibao Zhu","email":"","orcid":"","institution":"Yongkang Women and Children’s Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haibao","middleName":"","lastName":"Zhu","suffix":""},{"id":440738681,"identity":"9d6055a7-fcf0-4164-a2bc-824d03faada4","order_by":1,"name":"JunHua Zhang","email":"","orcid":"","institution":"Yongkang Women and Children’s Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"JunHua","middleName":"","lastName":"Zhang","suffix":""},{"id":440738682,"identity":"da6e1655-021c-4b9c-b30b-ca5bd828d8b3","order_by":2,"name":"Tingzhong Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACZjCCgQoIRwKfDh5ULWeI0cKArIWxDULj1WLPznv4dcEfmzz5iORnD7/Oq2M3OMB88DYPg10ebofxpVnP4EkrNryRZm4su42N2eAAW7I1D0NyMW4tPGbGPBKHEzfOSDCTltzGA9TCYybNw3AgsQGvFgOQlvRv0pJzJIBa+L8R0mL8mCfhcOJ8iRwzyY8NBiBb2PBrOcxjxsxzIC1xA8+bMmmGYwnMkofZjC3nGCTj1MLef8b4M88fm8T57enbJH/U1CXzHW9+eONNhR1OLUDABo4FgwsJDMzAWEqGRJMBbvVAwPwBRMr3H2Bg/MHAYIdX7SgYBaNgFIxIAAAGF0zDDCWSxgAAAABJRU5ErkJggg==","orcid":"","institution":"Yongkang Women and Children’s Health Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tingzhong","middleName":"","lastName":"Yang","suffix":""},{"id":440738683,"identity":"b346b946-ab5e-414a-8d76-25a6e6994f86","order_by":3,"name":"Sihui Peng","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Sihui","middleName":"","lastName":"Peng","suffix":""},{"id":440738684,"identity":"7f1b7a25-adcd-48c6-8dc1-8288dfe86676","order_by":4,"name":"Joan L Bottorff","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"L","lastName":"Bottorff","suffix":""}],"badges":[],"createdAt":"2024-11-23 08:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5508756/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5508756/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80281769,"identity":"754bc6f5-6555-46da-97b2-d8c47e970cb8","added_by":"auto","created_at":"2025-04-10 06:04:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":712377,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5508756/v1/82c1b2b4-d88a-4aa0-8024-ab2062487e76.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Which has an impact on which? Academic performance or mental health. A nationwide empirical study of 50 universities in China","fulltext":[{"header":"Background","content":"\u003cp\u003eAcademic performance refers to students\u0026rsquo; mastery of course content and skills, their ability to complete academic tasks, and their overall academic achievement. It is linked to individual development and well-being [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and impacts not only current students\u0026rsquo; academic successes at school but also their future career and work opportunities[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In education and health fields the association between academic performance and mental health has garnered increasing attention from researchers, policymakers, and practitioners. Many researchers suggest that there are close associations between academic performance and mental health, but the causal direction of the relationship between the two is inconsistent [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Many researchers have found that adolescent mental health problems not only cause pain and distress but also negatively affect their academic success [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. On contrary, some studies have shown that poor academic performance can lead to mental health problems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between mental health and academic performance is intricate and multifaceted, with numerous factors influencing its dynamics. It is important to provide a nuanced understanding of the mechanisms underlying the link between mental health and academic success [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, traditional education and health paradigms have often treated mental health and academic success as distinct domains. Understanding mechanisms of this relationship is essential for educational and health policy, practice, and intervention strategies aimed at promoting student well-being and academic success. Learning activities are cognitive processes. Research has shown that the impact of mental health on academic performance is mediated through cognitive deficits related to mental health problems. Some researchers have demonstrated that mental health influences students' motivation, self-efficacy, and self-regulation, which in turn have an impact on academic performance[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Others have pointed to the significance of affective states, such as anxiety and affect, in influencing motivation and self-regulation, highlighting their impact on cognitive processes and academic performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, mental health influences health behaviors such as sleep, nutrition, and physical activity, which are crucial for cognitive functioning and academic performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The exploration of a mechanism mediated by a third variable in the above is helpful for understanding the causality.\u003c/p\u003e \u003cp\u003eBuilding on these existing frameworks, the theory of mental stress offers another perspective on this relationship. In this context, academic stress, introduces as the third variable to explore the possible causal pathway between academic performance and mental health. According to the Stimulus-Response-Mental and Behavior Problem Theory (SRM), any stimulus can make people aware of the serious threat it poses and elicit a mental response, ultimately leading to mental and behavioral problems [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, academic stress is as the stimulus, while the behavioral and mental problems refer to academic achievement and mental disorders. Based on tis theoretical hypotheses, this study proposes two potential causal models: (1) \u003cem\u003eAcademic stress \u0026rarr; Academic performance\u0026rarr; Mental health\u003c/em\u003e (AAM) and (2) \u003cem\u003eAcademic stress\u0026rarr; Mental health \u0026rarr; Academic performance\u003c/em\u003e (AMA). As for AAM model, major causes of stress among students reported in the literature include high academic workload, lack of learning materials/resources, poor performance in examinations and difficulty reading and understanding modules. Adu (2023) reported that academic stress can have a negative impact on students' academic performance and health[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, Cao (2023) found that academic anxiety partially mediates the relationship between academic stress and academic burnout[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consequently, academic performance may contribute to mental health problems[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. There is also evidence to support the potential possibility of AMA model [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The correlation between high levels of academic stress and mental health or psychological well-being has been demonstrated in a number of studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, mental health problems have been shown to have a negative impact on academic performance [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study will explore the possibility of the existence of the two models mentioned above. The causal relationship between academic performance and mental health has long puzzled researchers in the academic community. Clarifying this issue holds significant implications for both academic research and practical applications in education and health policy.\u003c/p\u003e"},{"header":"Research Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eThis study employed a multistage sampling design.In the first stage, 60 universities with medical programs were selected as potential study sites as part of the Bloomberg Global Tobacco Control Advocacy Capacity Building Project[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These universities are located in 42 cities with different geographical regions of China mainland. 50 universities completed baseline surveys and passed the project evaluation (e.g., program availability, administrative cooperation, data completeness), including 22 medical schools and 28 comprehensive universities with medical programs. In the second stage, sample classes, which taking medical courses, within each university were identified. In the third stage, every student in these classes was designated as a study subject.A more detailaed description of the survey and the data can be found in Yang et al.(2015)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected using anonymous questionnaires. The questionnaire used in this study has previously been developed and named Global Health Professions Student Survey (GHPSS)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Responses were gathered in classrooms and took approximately 30 minutes to complete. This study was approved by the Ethics Committee of the Medical Center of Zhejiang University, and verbal consent was obtained from all participants.\u003c/p\u003e\n\u003ch3\u003eMeasurement\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eAcademic performance\u003c/strong\u003e \u003cp\u003eTo achieve stable measurement results, academic performance was measured using a relative method[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Participants were asked, \"What is your current academic performance position in your class?\", with responses ranging from \"top third,\" \"middle third,\" to \"bottom third,\" assigned values of 1, 2, and 3, respectively.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMental health\u003c/strong\u003e \u003cp\u003eMental health was assessed using the Chinese version of the Perceived Stress Scale (CPSS), which measures perceived stress levels over the past month [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This scale comprised 14 items that addressed perceptions of stress during the month prior to the survey. Items were rated on a 5-point Likert-type scale that ranged from 0 (never) to 4 (very often). Item scores were summed to yield a total stress score (Mental health), with higher scores indicating high risk of mental disorders. This scale has been widely used to assess stress in China and has been shown to be an appropriate indicator of mental health status [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAcademic stress\u003c/strong\u003e \u003cp\u003eAcademic stress in this study focus on medical professional concerns rather than strictly academic stress. Previous studies have reported that the acaedmic stress of current medical students is not only from teaching and learning-related stressors, but the concerns about the doctor-patien relationship[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and context related to professionalism[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It was reflected by participants' perceived stress related to the medical profession and the current state of medical education, closely related to learning motivation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These were measured through five questions. Participants were asked to indicate their perceved stress in following situations (1)The medical curriculum is extensive, and the learning tasks are heavy; (2)The learning content is tedious, and the teaching methods are monotonous; (3)Doctor-patient relationships are tense, making it difficult to be a doctor; (4)The work stress on doctors is high; (5)Studying medicine takes a long time and is costly. Responses were on a linked scale from \"strongly disagree\" to \"strongly agree,\" assigned values of 1 to 5. Item scores were summed to yield a total academic stress score. The Cronbach\u0026rsquo;s alpha is 0.803 in this study, indicating adequate reliability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eControl Variables\u003c/strong\u003e \u003cp\u003eDemographic variables included age, gender, ethnicity, family location, and major.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAll data were entered into a Microsoft Excel database and imported into SAS (version 9.4) for statistical analysis. Descriptive analyses were first conducted on sample distribution and variable attributes. To test the AAM and AMA models, this study constructed a mediation analysis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The mediation analysis was conducted using the Bootstrap test and the three-parameter comparison method. The significance of the mediation effect was determined based on the following criteria: (1) indirect effect (\u003cem\u003ea \u0026times;\u003c/em\u003e b) was tested using bootstrap resamples, the 95% confidence interval (CI) excluding zero indicated significance. (2) Path coefficients (\u003cem\u003ea\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e, and \u003cem\u003ec\u0026prime;\u003c/em\u003e) were tested (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Full mediation was indicated if \u003cem\u003ec\u0026prime;\u003c/em\u003e was non-significant, while partial mediation was identified if \u003cem\u003ec\u0026prime;\u003c/em\u003e remained significant but reduced[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. On this basis, SEM evaluation is based on the fit indices for the test of a single path coefficient and the overall model fit. Generally, the more fit indices applied to an SEM, the more likely that a miss-specified model will be rejected\u0026mdash;suggesting an increase in the probability of good models being rejected. Several criteria were used to evaluate the model fit including (1) χ\u003csup\u003e2\u003c/sup\u003e/df less than 3; (2) goodness-of-fit index (GFI), comparative fit index (CFI), normed fit index (NFI) and non-normed fit index (NNFI) greater than 0.9; (3) root mean square error of approximation (RMSEA) less than 0.05; (4)root mean square residual (RMR) less than 0.01. Note that due to the large sample size, standard errors were small, leading to large χ\u003csup\u003e2\u003c/sup\u003e values and small p-values. Therefore, this index was only used as a reference [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The analysis involves centralizing all variables, and the analysis will be conducted using the relevant procedures in SAS (9.4). All analyses were conducted with the university as the grouping unit to control for cluster effects.\u003c/p\u003e \u003cp\u003eAll analyses included weighting. Weights consisted of (1) sampling weight, the inverse of the sampling probability by school, and (2) post-stratification weight, adjusted according to the estimated gender distribution in the national survey[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The final weight was the product of these two weights. Nonresponse weight was not used due to the very low nonresponse rate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe completion rate of valid questionnaires in this study was 97.8%, with a sample comprising 11,659 students from 50 different universities. Among the sample, 20.2% were under 19 years old, 18.8% were 22 years old or above, and the majority were between 20 and 21 years old. Males constituted 33.2% of the sample, while females made up 66.8%. Most of the respondents (72.2%) were sophomores and juniors. The majority of the sample (86.0%) were Han Chinese (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Composition of the Sample (N\u0026thinsp;=\u0026thinsp;11,659)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th Year and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily Location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMajor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the descriptive statistics of the variables used in the models. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of mediation analyses. In the AAM model, the Bootstrap 95% CI (0.000\u0026thinsp;~\u0026thinsp;0.002) included zero, indicating that academic performance does not significantly mediate the relationship. However, the comparison of the three key parameters\u0026mdash;a, b, and c\u0026rsquo;\u0026mdash;revealed that the direct effects of a*b and c\u0026rsquo; were statistically significant and consistent in coefficient value of c. This suggests a partial mediating effect for academic performance. In this model, the mediating effect of academic performance is only 1.6%, indicating that its influence on the relationship between academic stress and mental health is minimal. In contrast, AMA model passed both Bootstrap and three-parameter comparison method test. Since the 95% Bootstrap CI (0.003\u0026thinsp;~\u0026thinsp;0.008) did not include zero, this confirms a significant mediating effect of mental health. Moreover, the statistical significance of parameters, a, b, and c\u0026rsquo;, the direct effects of a*b and c\u0026rsquo; are the same with c, which indicates that there is a mediating effect. In this model, the ratio of the mediating effect to the total effect was 17.7%, indicating that mental health plays a much larger role in mediating the relationship between academic stress and academic performance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical value of variables in models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u0026ndash;12.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u0026ndash;4.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ12per\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u0026ndash;3.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u0026ndash;12.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis results of mediation effect in AAM and AMA model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal effect value(C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMediation effect value (a*b\u003c/p\u003e \u003cp\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea*b (95% Boot CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDirect effect value (C\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAM model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u0026thinsp;~\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.062**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMA model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u0026thinsp;~\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed that summary of model fit statistics for structural models. In the AAM model, the resulting model revealed: χ\u003csup\u003e2\u003c/sup\u003e/df:188.400, GFI: 0.984, RMSEA: 0.127, RMR: 0.312, CFI: 0.150, NFI: 0.157, NNFI:0.550. With the exception of GFI, all fit indices failed to meet the criteria for a good model fit, suggesting that the AAM model does not provide an adequate fit to the data. But in the AMA model, the results were different: χ\u003csup\u003e2\u003c/sup\u003e/df: 7.32, GFI: 0.984, RMSEA: 0.999, RMR: 0.032, CFI: 0.971, NFI: 0.967, NNFI: 0.914.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThat results of goodness-of-fit evaluation in AAM and AMA model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNNFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAM model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMA model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMedical education is the education aimed at cultivating professionals in the field of medicine. Academic performance and mental health problems have always been of concern to researchers, decision-makers, and practitioners in the health and education fields. The causal relationship between them has long puzzled scholars. Clarifying this issue is of great significance both academically and in practice [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study examined the possibility of the existence of models by comparing and analyzing the mediating effects in AAM and AMA model. The bootstrap test showed no mediating effect in the AAM model, whereas a significant mediation effect was found in the AMA model. This implies that the AAM model is not valid, while the AMA model is valid. Furthermore, the ratio of the mediating effect to the total effect was 1.6% in AAM model and 17.7% in AMA model and, indicating a much higher proportion in the latter compared to the former. Mental health plays a much larger role in mediating the relationship between academic stress and academic performance. AAM model did not meet the criteria for a good model fit. However, in AMA model, most indicators met the criteria for a good fit, indicating that the model is in a good fit state. The finding supports the validity of the AMA model, aligning with our hypothesis on the causal path from mental health to academic performance. The successful establishment of the AMA model suggests a plausible causal pathway in which mental health problems lead to poorer academic outcomes. This finding addresses a longstanding debate regarding the causal relationship between academic performance and mental health [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This association can be explained by the SRM Theory, which offers a window into the possible mechanisms that may link overuse of academic stress and poor academic performance though mental health problems[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This result is also supported by several prior empirical studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While this study introduced academic stress and mental health as predictors of academic performance, it is important to acknowledge that these constructs are also influenced by a broader range of individual, environmental, and contextual factors that were beyond this study\u0026rsquo;s scope. The AAM and AMA models were developed to examine the directional relationships among academic stress, mental health, and academic performance, rather than to quantify relative predictive values of different contributors. Nonetheless, these findings highlight the importance of addressing learning challenges from a mental health perspective as a strategy to support academic success. Given this, universities need to provide adequate and accessible mental health services for students. In Brazil, for example, initiatives such as the Psychosocial Care Space (Epsico) at the Universidade do Estado do Amazonas (UEA) and the Psychological Assistance Project at the Pontif\u0026iacute;cia Universidade Cat\u0026oacute;lica de Minas Gerais (APP/PUC Minas) exemplify institutional responses driven by administrative recognition of student mental distress, leading to the implementation of structured mental health support strategies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Supporting this perspective, Phillips et al. (2022) found that more than one-third of medical students who perceived a need for mental health support did not receive any services. Their findings emphasize the importance of targeted outreach, particularly for underserved student populations, and highlight how reducing structural barriers, such as rigid academic schedules, are able to significantly enhance help-seeking behaviors and overall student well-being [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. University environments that promote the mental well-being are also important[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This could include initiatives that have been shown to shift learning environments towards supporting student mental wellbeing such as building green environments, providing a range of extracurricular activities for students, strengthening interactions between teachers and students, as well as among students, and arranging study schedules to ensure there is enough rest time. Special university campus-based clinics should be established to provide mental health counseling to high-risk individuals. Offering stress management programs and providing mental health services where students can share their mental health concerns, engage in discussions with others facing problems, and learn stress management skills are also important initiatives for improving student mental health and building a positive campus community.\u003c/p\u003e \u003cp\u003eThis study shows that the β value for the relationship between academic stress and mental health is 0.062, while it is 0.005 for academic stress and academic performance. This suggests that academic stress has a greater impact on student mental health than academic performance. This is consistent with other studies that show high levels of academic stress have an impact on mental health[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study further confirms the importance of addressing mental health problems among medical students from another perspective. Currently, medical students in China are faced with a heavy workload of courses, many of which are dull and taught using a single, uninteresting method. The intense academic stress on medical students has led to prominent mental health problems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Efforts should be made to streamline and optimize medical courses. Courses should focus on core concepts, basic theories, and fundamental skills. There should be mechanisms in place to control the expansion of course content. Additionally, teaching methods should be innovated with the advent of the artificial intelligence era. Artificial intelligence technology can assist teachers in personalizing instruction, making teaching methods more flexible and efficient, thereby enhancing students' learning outcomes and experiences. In related development, Chen et al. (2023) designed and implemented a chatbot teaching assistant that effectively addressed students\u0026rsquo; learning needs. The chatbot led to significant improvements in student performance on tasks requiring basic memory retention and conceptual understanding [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Fatima et al. (2023), in their scoping review, emphasized that the integration of AI in education has accelerated the use of advanced AI-driven methods, deep learning and virtual reality as examples, in medical training. They found that AI-based approaches can substantially enhance the practical skills of medical students [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. AI shows great promise in enhancing teaching and learning, its implementation must be approached with careful consideration of ethical issues, privacy protection, data security, and equity to ensure responsible and inclusive adoption[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt should be noted that it is also possible to present the opposite direction from academic achievement to academic stress. For example, poor academic performance may lead to a sense of frustration with learning, thereby increasing academic stress. We must clarify that in this study, academic stress and academic performance are completely different concepts. The former refers to participants' perceived stress related to the medical profession and the current state of medical education, such as feeling burdened by a heavy academic workload, lacking interest in their field, and finding the content monotonous and unappealing. The latter refers to students' mastery of course content and skills, their ability to complete academic tasks, and their overall academic achievement. According to SRM Theory, academic stress is the stimulus, and academic achievement is the behavioral outcome. The logical model of AMA, established in SRM hypothesis, has already been validated. Furthermore, we conducted a mediation analysis and performed a fit examination for the reverse AAM pathway (mental health \u0026rarr; academic performance \u0026rarr; academic stress, RMAA) and the AMA pathway (academic performance \u0026rarr; mental health \u0026rarr; academic stress, RAMA). In RMAA, the results revealed a mediating effect of -0.031, with a Bootstrap 95% CI of (-0.008, 0.001), indicating that academic performance does not significantly mediate the relationship. The overall fit of the RMAA and RAMA models is poorer than that of the AMA model. This suggests that the overall fit of the AMA model is better, indicating that the model we hypothesized in this study is appropriate. According to the above results, the pathway from academic performance to academic stress should represent a feedback effect of behavioral outcomes, rather than a mainstream effect. However, the conclusions drawn here are based solely on theoretical and statistical analysis, and we cannot definitively determine solo causal links between these associations. There remains a need for further longitudinal, in-depth, and comprehensive exploration of the issues related to academic stress and its influence on academic performance and mental health.\u003c/p\u003e \u003cp\u003eThe study has some limitations. While this research is theoretically grounded and supported by empirical evidences, the methodological limitation lies in the fact that it is a cross-sectional study design, which cannot fundamentally establish causal links between academic performance and mental health. Further research should involve a complex design that combines quantitative and qualitative methods, with longitudinal observations. This is expected to provide further clarification on causal relationships. A second study limitation is that AMA model in this study may not be the only model that is useful for testing the causal relationship between academic performance and mental health. There may be other models\u0026rsquo; worth further exploration. Another limitation of this study is that academic performance was self-reported by participants. Although self-reported academic performance is widely used in empirical research and has shown reasonable reliability[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, it may be prone to biases such as social desirability and memory errors. Future research should consider incorporating objective indicator, such as the cumulative grade point average (GPA) over the course of students\u0026rsquo; studies to enhance accuracy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides new and direct evidence that mental health has stronger impact on academic performance than the reverse. This directional relationship highlights the foundational role of mental health in supporting students\u0026rsquo; academic success. Therefore, educational policies and curricula should prioritize mental health support as a central component, potentially leading to both improved well-being and enhanced academic outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee at the Zhejiang University Medical Center (Approval number: 2014:1-017). Verbal informed consent was obtained from all participants before data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable. This study does not include identifiable participant data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank local teams from the \u0026ldquo;Facilitate MOH endorsement of tobacco control implementation through promoting tobacco control advocacy capacity in medical schools \u0026ldquo;project (supported by UNION)\u0026rdquo; for organizing the data collection. The project universities, local government, and CDC also partly funded this survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported, in part, by Global Bridges/IGLC, 2014SC1(13498319)\u003cbr\u003e \u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePascoe MC, Hetrick SE, Parker AG: The impact of stress on students in secondary school and higher education. \u003cem\u003eInternational Journal of Adolescence and Youth\u0026nbsp;\u003c/em\u003e2020, 25(1):104-112.\u003c/li\u003e\n \u003cli\u003eMappadang A, Khusaini K, Melan S, and Elizabeth E: Academic interest determines the academic performance of undergraduate accounting students: Multinomial logit evidence. \u003cem\u003eCogent Business \u0026amp; Management\u0026nbsp;\u003c/em\u003e2022, 9(1):2101326.\u003c/li\u003e\n \u003cli\u003eDuncan MJ, Patte KA, Leatherdale ST: Mental health associations with academic performance and education behaviors in Canadian secondary school students. \u003cem\u003eCanadian Journal of School Psychology\u0026nbsp;\u003c/em\u003e2021, 36(4):335-357.\u003c/li\u003e\n \u003cli\u003eMahdavi P, Valibeygi A, Moradi M, Sadeghi S: Relationship Between Achievement Motivation, Mental Health and Academic Success in University Students. \u003cem\u003eCommunity Health Equity Res Policy\u0026nbsp;\u003c/em\u003e2023, 43(3):311-317.\u003c/li\u003e\n \u003cli\u003eSingh RK, Goswami S: An Intricate Relationship Between Mental Health and Academic Success of Students at Secondary Level: A Comprehensive Review. \u003cem\u003eShodhshauryam, International Scientific Refereed Research Journal\u0026nbsp;\u003c/em\u003e2024, 7(2):14-24.\u003c/li\u003e\n \u003cli\u003eZhang W, Dia J, Wang H, T Y: Impact of uncertainty stress on academic performance among medical students: A nationwide experimental study of 50 universities. \u003cem\u003eChina higher medical education\u0026nbsp;\u003c/em\u003e2023(12):60-63.\u003c/li\u003e\n \u003cli\u003eChu T, Liu X, Takayanagi S, Matsushita T, Kishimoto H: Association between mental health and academic performance among university undergraduates: The interacting role of lifestyle behaviors. \u003cem\u003eInternational journal of methods in psychiatric research\u0026nbsp;\u003c/em\u003e2023, 32(1):e1938.\u003c/li\u003e\n \u003cli\u003eEisenberg D, Golberstein E, Hunt JBJTBjoea, policy: Mental health and academic success in college. \u003cem\u003eJournal of Economic Analysis \u0026amp; Policy\u0026nbsp;\u003c/em\u003e2009, 9(1):1-35.\u003c/li\u003e\n \u003cli\u003eGall G, Pagano ME, Desmond MS, Perrin JM, Murphy JM: Utility of psychosocial screening at a school-based health center. \u003cem\u003eThe Journal of school health\u0026nbsp;\u003c/em\u003e2000, 70(7):292-298.\u003c/li\u003e\n \u003cli\u003eMasi G, Tomaiuolo F, Sbrana B, Poli P, Baracchini G, Pruneti CA, Favilla L, Floriani C, Marcheschi M: Depressive symptoms and academic self-image in adolescence. \u003cem\u003ePsychopathology\u0026nbsp;\u003c/em\u003e2001, 34(2):57-61.\u003c/li\u003e\n \u003cli\u003eSuldo S, Thalji A, Ferron J: Longitudinal academic outcomes predicted by early adolescents\u0026apos; subjective well-being, psychopathology, and mental health status yielded from a dual factor model. \u003cem\u003eThe Journal of Positive Psychology\u0026nbsp;\u003c/em\u003e2011, 6(1):17-30.\u003c/li\u003e\n \u003cli\u003eStewart T, Suldo S: Relationships between social support sources and early adolescents\u0026apos; mental health: The moderating effect of student achievement level. \u003cem\u003ePsychology in the Schools\u0026nbsp;\u003c/em\u003e2011, 48(10):1016-1033.\u003c/li\u003e\n \u003cli\u003eTempelaar WM, de Vos N, Plevier CM, van Gastel WA, Termorshuizen F, MacCabe JH, Boks MPM: Educational Level, Underachievement, and General Mental Health Problems in 10,866 Adolescents. \u003cem\u003eAcademic pediatrics\u0026nbsp;\u003c/em\u003e2017, 17(6):642-648.\u003c/li\u003e\n \u003cli\u003eSevil J, Pr\u0026aacute;xedes A, Abarca-Sos A, Del Villar F, Garc\u0026iacute;a-Gonz\u0026aacute;lez L: Levels of physical activity, motivation and barriers to participation in university students. \u003cem\u003eThe Journal of sports medicine and physical fitness\u0026nbsp;\u003c/em\u003e2016, 56(10):1239-1248.\u003c/li\u003e\n \u003cli\u003eKuhl J: Chapter 5 - A Functional-Design Approach to Motivation and Self-Regulation: The Dynamics of Personality Systems Interactions. 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In: \u003cem\u003eIntelligent Communication Technologies and Virtual Mobile Networks: 2023// 2023; Singapore\u003c/em\u003e: Springer Nature Singapore; 2023: 571-591.\u003c/li\u003e\n \u003cli\u003eWu D, and Yang T: Late bedtime, uncertainty stress among Chinese college students: impact on academic performance and self-rated health. \u003cem\u003ePsychology, Health \u0026amp; Medicine\u0026nbsp;\u003c/em\u003e2023, 28(10):2915-2926.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental Health, Academic Performance, Medical students, Occupational stress, Structural Equation Modeling","lastPublishedDoi":"10.21203/rs.3.rs-5508756/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5508756/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile numerous studies have identified a strong association between academic performance and mental health, the direction of causality remains unclear. This study aims to examine whether academic performance impacts mental health or vice versa.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multistage sampling survey was conducted among 11,659 medical students from 50 universities across the country. Two mediation models were constructed for analysis: (1) \u003cem\u003eAcademic stress \u0026rarr; Academic performance \u0026rarr; Mental health\u003c/em\u003e (AAM), and (2) \u003cem\u003eAcademic stress \u0026rarr; Mental health \u0026rarr; Academic performance\u003c/em\u003e (AMA). Data were analyzed using descriptive statistics and mediation analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe Bootstrap test showed that the 95% confidence interval (CI) ranged from 0.000 to 0.002 in AAM model and 0.003 to 0.008 in AMA model. Academic performance does not have a mediating effect in AAM, whereas in the AMA, mental health serves as a mediator. Although both the AAM and AMA models showed mediating effects by using the three-parameter comparison method, the ratio of the mediating effect to the total effect was 1.6% in the AAM model and 17.7% in the AMA model. Model fit indices in SEM revealed that, aside from the goodness-of-fit index (GFI), the AAM model did not meet acceptable fit criteria. In contrast, the AMA model showed good fit across most indices.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings support the AMA model, suggesting a more plausible causal pathway in which mental health significantly affects academic performance. This insight underscores the importance of addressing mental health concerns to enhance students\u0026rsquo; academic outcomes, offering valuable guidance for educational policy and student support interventions.\u003c/p\u003e","manuscriptTitle":"Which has an impact on which? Academic performance or mental health. 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