Medical students are exposed to a distinctive concentration of academic demands. Their training requires rapid acquisition of large volumes of biomedical and clinical knowledge, repeated examinations, frequent performance comparisons, long contact hours, clinical placements, and progression decisions that can carry strong professional and personal meaning. Academic pressure is therefore not a single exposure. It includes perceived workload, time scarcity, examination pressure, fear of poor performance, curriculum density, competition, teacher evaluation, uncertainty about progression, and a mismatch between demands and perceived coping resources. Within a transactional stress framework, psychological harm becomes more likely when educational demands are appraised as exceeding available resources, especially when the demands are persistent or perceived as uncontrollable [8]. The mental-health burden in medical education is well established. Earlier systematic reviews reported substantial levels of psychological distress among medical students [6], while meta-analytic estimates suggested that approximately 28% experience depression or depressive symptoms [3] and about one third experience anxiety [5]. A large JAMA meta-analysis similarly demonstrated a high pooled prevalence of depressive symptoms and suicidal ideation [4]. These prevalence estimates, however, do not by themselves establish which features of medical education are most closely associated with symptoms. For prevention, the distinction matters: a high prevalence may reflect personal, social, financial, biological, or educational factors, whereas institutional interventions require evidence that modifiable academic conditions are meaningfully related to mental health. Academic assessment has long been implicated as one such modifiable condition. Lyndon and colleagues systematically reviewed the relationship between assessment and psychological distress and found that medical students commonly experienced greater stress and anxiety around examinations and assessment periods [2]. Subsequent primary studies have expanded the exposure beyond examinations to include curriculum overload, difficulty coping with the syllabus, learning-environment stressors, academic performance concerns, and educational stress scales. For example, repeated measurement in Saudi medical students showed that depression, anxiety, and stress were all higher before major examinations than during regular classes [9], while recent multicenter evidence from Bangladesh demonstrated graded increases in academic stress with increasing severity of both depression and anxiety [16]. A second challenge is measurement heterogeneity. Academic pressure may be captured using the Medical Student Stressor Questionnaire (MSSQ), the Academic Stress Scale, stress-factor inventories, single-item examination contexts, or institution-specific questions. Mental health is likewise measured using PHQ-9, GAD-7, DASS-21, GHQ-12, HADS, Goldberg scales, or depressive-mood instruments. A 2026 review of psychological-distress tools in medical students underscored the breadth of measurement approaches used in this field [7]. Such heterogeneity can obscure the underlying strength of association if all studies are reduced to prevalence alone or if statistically incompatible estimates are combined without distinction. The present systematic review and meta-analysis therefore focused on the relationship between academic pressure and depression, anxiety, and psychological distress rather than on prevalence alone. The primary objective was to identify and synthesize quantitative studies in medical students that measured an academic-specific exposure and a relevant mental-health outcome. The secondary objective was to pool compatible association estimates using a transparent random-effects framework and to examine the consistency of findings across examination-based, curriculum-based, and scale-based representations of academic pressure.

This review was prepared in accordance with the reporting principles of PRISMA 2020 [1]. The review question was framed around medical students as the population, academic pressure or academic-specific stress as the exposure, and depression, anxiety, or general psychological distress as the outcomes. A protocol and analytic plan were specified before quantitative pooling, but the protocol was not prospectively registered in PROSPERO. The search covered literature from database inception through January 2026. Searches were conducted across PubMed/MEDLINE-indexed records, PubMed Central, publisher-hosted scholarly pages and citation trails. Search concepts were combined around medical student terms with academic pressure, academic stress, examination stress, workload, assessment, curriculum burden, academic performance, learning environment, depression, depressive symptoms, anxiety, psychological distress, DASS, PHQ, GAD, GHQ, HADS and MSSQ. Backward and forward citation checking was used to identify studies that might not be retrieved by exact terminology. The search was restricted to English-language full reports for this manuscript. Studies were eligible when they reported quantitative data from undergraduate medical students, or a predominantly medical-training sample in which medical-student academic stressors were central to the analysis; measured an academic-specific exposure such as workload, curriculum pressure, examinations, assessment, academic performance concerns, syllabus coping, learning-environment stressors or a validated academic-stress domain; and measured depression, anxiety or broader psychological distress using a recognized instrument or clearly defined symptom scale. Cross-sectional, repeated-measures and longitudinal designs were eligible. Studies measuring only undifferentiated life stress without an academic component, qualitative studies, reviews, editorials, protocols, reports without a relevant mental-health outcome, and overlapping reports that did not contribute independent information were excluded. Mixed trainee samples were retained for narrative context only when the medical-student component and academic stressor construct were central; they were not pooled unless an effect estimate was compatible and attributable to the target population. Data extraction captured country, design, sample size, stage of training, academic-pressure construct, mental-health instrument, timing in relation to examinations, and the principal association estimate. For the quantitative synthesis, Pearson correlations were preferred. Rank-biserial correlations were treated as correlation-type effects because they directly quantify standardized group separation on an ordinal/continuous outcome. In a prespecified secondary synthesis of psychological distress, a standardized structural path coefficient was treated as correlation-compatible and was explicitly distinguished from Pearson-r-only pooling. Odds ratios, prevalence contrasts, mean score changes, and regression estimates without a common conversion basis were narratively synthesized rather than forced into a single effect metric. Methodological quality was assessed using domains adapted from the Joanna Briggs Institute approach for analytical cross-sectional evidence [27], with attention to sampling, clarity of exposure measurement, validity of outcome measurement, identification and management of confounding, completeness of reporting, and appropriateness of statistical analysis. Studies were categorized as low risk, some concerns, or high risk of bias. No study was excluded solely because of risk of bias; instead, limitations were considered in interpretation and sensitivity analyses. For meta-analysis, correlation coefficients were transformed using Fisher's z transformation, pooled using inverse-variance weighting under a DerSimonian-Laird random-effects model [28], and back-transformed to r for interpretation. Between-study heterogeneity was assessed with Cochran's Q, I² and τ² [29]. Approximate magnitudes were interpreted descriptively as small around r=0.10, moderate around r=0.30 and large around r=0.50, without treating these thresholds as rigid clinical cutoffs. Leave-one-out analyses were used to assess the stability of the depression and psychological-distress estimates. Because fewer than 10 studies contributed to every outcome-specific meta-analysis, formal small-study-effect tests and funnel-plot asymmetry tests were not performed; at such low k, those methods are underpowered and potentially misleading. Statistical calculations were independently reproduced using Fisher-z formulas and standard random-effects equations; results are presented to three decimal places for pooled associations and one decimal place for I².

The focused review identified 264 records, comprising 248 records from indexed/public database and publisher interfaces and 16 additional records identified through citation searching. After removal of 58 duplicates, 206 titles and abstracts were screened and 155 were excluded. Fifty-one reports were sought in full text, three were not retrieved, and 48 full-text reports were assessed. Thirty reports were excluded because they lacked an academic-specific exposure (n=11), did not contain a relevant depression, anxiety or distress outcome (n=6), had an ineligible population (n=4), were reviews/editorials/protocols (n=3), did not provide sufficient relationship data (n=4), or represented an overlapping cohort without independent contribution (n=2). Eighteen studies involving 8,503 participants were retained in the systematic review, and five unique studies contributed to one or more pooled association analyses. The included evidence spanned Pakistan, Saudi Arabia, Mexico, Nepal, Sri Lanka, Malaysia, India, Germany, Türkiye, Bangladesh and China, with publication years from 2010 to 2026. Most studies were cross-sectional. One German study included repeated cross-sectional and longitudinal follow-up over the first 1.5 years of medical training [15], and the Saudi examination study assessed students at two educational time points [9]. Academic pressure was operationalized through validated instruments such as the MSSQ, Academic Stress Scale and Medical Student Stress Factor Scale; through specific domains such as inability to cope with the syllabus or perceived academic performance; or through a natural exposure contrast between regular teaching and high-stakes examinations. Mental-health outcomes were measured using DASS-21, PHQ-9, GAD-7, HADS, GHQ-12, Goldberg anxiety/depression scales, and depressive-mood measures. The direction of association was strikingly consistent. In Saudi Arabia, the pre-examination prevalence of depression, anxiety and stress was 43%, 63% and 41%, respectively, compared with 30%, 47% and 30% during regular classes [9]. In Tamil Nadu, symptom scores increased sharply on the examination day: mean stress increased from 12.70 to 21.27, anxiety from 11.27 to 24.06, and depression from 6.88 to 10.23; reported odds ratios for the examination-day state were 2.153 for stress, 3.038 for anxiety and 2.513 for depression [17]. These repeated or examination-proximal observations support a temporal relationship between acute assessment pressure and symptom escalation, although neither study can fully separate exam pressure from other time-varying factors. Scale-based studies showed similar patterns. Among 632 medical students in Türkiye, the learning-environment/academic-performance component of the MSSF correlated with DASS-21 depression (r=0.402), anxiety (r=0.452) and stress (r=0.501), all p<0.001 [14]. In 1,072 Bangladeshi medical students, academic stress was present in 47.5%; students with academic stress had higher median anxiety scores (11 versus 7; rank-biserial r=0.31) and depression scores (12 versus 7; r=0.43), both p<0.001 [16]. Severe depression showed an adjusted odds ratio of 21.54 (95% CI 7.21-64.38) for academic stress, while moderate anxiety showed an adjusted odds ratio of 3.95 (95% CI 1.98-7.90) [16]. The directionality of that cross-sectional model is statistical rather than causal, but the graded relationship supports strong covariation between academic strain and symptom severity. Academic workload and curriculum difficulty were also repeatedly implicated outside the pooled datasets. Among 240 medical undergraduates in West Bengal, inability to cope with the MBBS syllabus was independently associated with depression (AOR=2.54, 95% CI 1.26-5.12) and anxiety (AOR=2.27, 95% CI 1.19-4.34) [22]. In a multicenter Indian study of 625 students, academic pressure was reported as a stress trigger by 56.8% and examination stress by 61.3% [24]. In Mexico, professor evaluations and reading/task overload were among the most frequently reported academic stressors in a cohort where anxiety and depression screening positivity was high [19]. A 2026 Chinese mediation study likewise found academic stress positively related to depressive mood, with rumination and ego depletion jointly accounting for a substantial proportion of the indirect association [23]. Table 1. Characteristics and principal findings of included studies Study Setting/design N Academic pressure measure/context Mental-health outcome Key finding Shah et al., 2010 [26] Pakistan; cross-sectional 161 Perceived stress and academic sources Perceived stress questionnaire High perceived stress; curriculum/examinations and academic demands were prominent sources. Kulsoom & Afsar, 2015 [9] Saudi Arabia; repeated assessment 575 Major examination vs regular classes DASS-21 Pre-exam depression/anxiety/stress 43%/63%/41% vs 30%/47%/30% during regular classes. Romo-Nava et al., 2019 [10] Mexico; cross-sectional 1,068 Perceived academic stress in exam season PHQ-9 Depressive severity increased with perceived academic stress; MDD prevalence 16.2%. Pokhrel et al., 2020 [20] Nepal; cross-sectional medical students/residents 651 MSSQ academic/teaching-learning stressors HADS; burnout inventory Anxiety 45.3%, depression 31%; academic and teaching-learning stressors were prominent correlates. Jayarajah et al., 2020 [11] Sri Lanka; cross-sectional validation 603 MSSQ academic-related stressor domain GHQ-12 Academic-related stressor domain correlated strongly with GHQ-12 (r=0.628, p<0.001). Yusoff et al., 2021 [12] Malaysia; cross-sectional SEM 241 MSSQ academic stress DASS-21 psychological distress Academic stress significantly increased psychological distress; standardized path coefficient used in secondary synthesis. Mirza et al., 2021 [13] Saudi Arabia; cross-sectional 231 Academic achievement and combined academic/non-academic stressors DASS-21 Depression 55.8%, anxiety 45.9%, stress 37.7%; academic achievement was an important explanatory factor. Avila-Carrasco et al., 2023 [19] Mexico; cross-sectional 728 SISCO-II academic stress; professor evaluation/task overload Goldberg anxiety/depression Anxiety 67.9%, depression 81.3%; professor evaluation and task/reading overload were frequent academic stressors. Rajanayagam et al., 2023 [17] India; exam-period comparison 150 Relaxed period vs examination day DASS-10; salivary cortisol Stress, anxiety and depression scores all increased on examination day; cortisol also increased. Panja et al., 2023 [18] India; cross-sectional 125 MSSQ-40 Academic stress severity 79% experienced high-to-severe academic stress; higher multidomain stress accompanied poorer academic performance. Arnold et al., 2025 [15] Germany; repeated/longitudinal 226 baseline Academic stress across first 1.5 years Depressive symptoms Academic stress predicted depressive symptoms at baseline (β=0.24), FUM1 (β=0.60) and FUM2 (β=0.51). Akova et al., 2025 [14] Türkiye; cross-sectional validation 632 MSSF learning environment/academic performance DASS-21 Correlations with depression r=0.402, anxiety r=0.452 and stress r=0.501; all p<0.001. Karim et al., 2026 [16] Bangladesh; multicenter cross-sectional 1,072 ASS-40 academic stress PHQ-9; GAD-7 Academic stress 47.5%; depression r=0.43 and anxiety rank-biserial r=0.31 across stress groups; graded adjusted associations. Khan Majlish et al., 2026 [25] Bangladesh; cross-sectional 380 Academic and psychological determinants Mental stress questionnaire/model Academic and psychological determinants significantly differentiated moderate/high stress categories. Rathi et al., 2026 [21] India; cross-sectional year-wise 311 MSSQ academic stressors Stress-domain severity Academic-related stress varied significantly by academic year and was greatest in later training. Ganguly et al., 2026 [22] India; cross-sectional 240 Ability to cope with syllabus; academic performance DASS-21 Inability to cope with syllabus independently associated with depression (AOR 2.54) and anxiety (AOR 2.27). Zhuo et al., 2026 [23] China; cross-sectional SEM 484 Academic stress Depressive mood Positive association; rumination and ego depletion mediated much of the relationship. Singh et al., 2026 [24] India; multicenter cross-sectional 625 Academic pressure and examination stress triggers Perceived Stress Scale 56.8% reported academic pressure and 61.3% exam stress as triggers; 55.5% had moderate stress. Risk-of-bias appraisal suggested that five studies were at relatively low risk, twelve had some concerns, and one had high risk, primarily because of sampling limitations, cross-sectional exposure-outcome measurement, incomplete adjustment for confounding, or nonstandard analytical choices. The exam-day study by Rajanayagam et al. was rated high risk for causal interpretation because measurements were taken in the same cohort but analyzed with methods that did not fully exploit paired data [17]. The principal strength across the evidence base was the frequent use of validated mental-health instruments; the main recurring weakness was the dominance of cross-sectional designs, which precludes determining whether academic pressure causes symptoms, symptoms amplify perceptions of pressure, or both processes occur simultaneously. Table 2. Overall risk-of-bias judgement for included studies Study Judgement Principal limitation/strength Shah 2010 Some concerns Single institution; self-reported exposure/outcome. Kulsoom 2015 Some concerns Repeated measurements but incomplete pairing/attrition across time points. Romo-Nava 2019 Low Large representative sample; validated PHQ-9; adjusted analysis. Pokhrel 2020 Some concerns Mixed medical-student/resident sample for some estimates. Jayarajah 2020 Low Large sample; validated concurrent outcome; clear analysis. Yusoff 2021 Some concerns Purposive sample and cross-sectional SEM. Mirza 2021 Some concerns Single setting; combined academic/non-academic stressors in some models. Avila-Carrasco 2023 Some concerns Pandemic-specific context; incomplete SISCO response. Rajanayagam 2023 High Small sample; paired exposure context analyzed with non-paired methods. Panja 2023 Some concerns Small single-institution first-year sample. Arnold 2025 Low Repeated follow-up and longitudinal modeling; attrition considered. Akova 2025 Low Large sample; validated instruments and detailed psychometric analysis. Karim 2026 Low Multicenter stratified sample; validated scales; multivariable/SEM analyses. Khan Majlish 2026 Some concerns Cross-sectional design and self-report. Rathi 2026 Some concerns Cross-sectional institution-based sample. Ganguly 2026 Some concerns 46% response rate; single institution. Zhuo 2026 Some concerns Cross-sectional mediation cannot establish temporal mediation. Singh 2026 Some concerns Convenience sampling; stress outcome rather than diagnostic symptoms. For depressive symptoms, three compatible studies involving 1,930 participants were pooled. The random-effects association was r=0.374 (95% CI 0.283-0.458; p<0.001), indicating a moderate positive relationship between academic pressure and depressive symptoms. Heterogeneity was substantial (Q=8.54, df=2, p=0.014; I²=76.6%; τ²=0.0060). Leave-one-out analyses remained positive in every case, with pooled estimates ranging approximately from r=0.330 to r=0.420, indicating that the direction of effect was not dependent on any single study. The pooled result is shown in Figure 2. For anxiety, two studies involving 1,704 participants provided compatible correlation-type data. The pooled random-effects association was r=0.382 (95% CI 0.234-0.512; p<0.001). Heterogeneity was high (Q=11.00, df=1, p<0.001; I²=90.9%; τ²=0.0126), reflecting the difference between the Turkish continuous correlation and the Bangladeshi rank-biserial contrast. Despite this heterogeneity, both individual estimates were positive and clinically coherent with the narrative evidence from examination-based studies. The pooled result is shown in Figure 3. A secondary synthesis examined broader psychological distress or stress. Three studies with 1,476 participants contributed standardized association estimates. The pooled correlation-compatible association was 0.515 (95% CI 0.374-0.633; p<0.001), with Q=22.03, df=2, p<0.001, I²=90.9% and τ²=0.0219. This analysis should be interpreted more cautiously because Yusoff et al. contributed a standardized SEM path coefficient rather than a Pearson correlation [12]. Leave-one-out estimates remained positive and ranged from approximately 0.452 to 0.568. Thus, even under sensitivity analysis, academic pressure remained meaningfully associated with broader distress. The pooled result is shown in Figure 4. Formal publication-bias testing was not conducted because each outcome-specific meta-analysis contained fewer than 10 studies. With such small numbers, funnel-plot appearance and regression tests can be dominated by chance and heterogeneity. The limited number of poolable studies is itself an important finding: the field contains many studies describing prevalence and stressor frequency, but comparatively few report association statistics in a format suitable for direct quantitative synthesis. Table 3. Summary of quantitative meta-analyses Outcome k N Pooled association 95% CI Q I² τ² Interpretive note Depressive symptoms 3 1,930 r=0.374 0.283-0.458 8.54 76.6% 0.0060 Pearson/correlation-type; random effects Anxiety symptoms 2 1,704 r=0.382 0.234-0.512 11.00 90.9% 0.0126 Pearson + rank-biserial correlation-type Psychological distress/stress 3 1,476 standardized=0.515 0.374-0.633 22.03 90.9% 0.0219 Secondary correlation-compatible synthesis; includes one standardized path coefficient

This systematic review found a consistent relationship between academic pressure and adverse mental-health outcomes in medical students. The pooled association with depression was r=0.374 and with anxiety was r=0.382, both in the moderate range. Broader psychological distress or stress showed a larger correlation-compatible pooled association of approximately 0.52, although that secondary estimate combined slightly different standardized metrics and therefore warrants greater caution. Importantly, the meta-analytic findings agree with the non-pooled evidence: examination periods, curriculum overload, poor perceived ability to cope with the syllabus, professor evaluations and academic-performance concerns repeatedly coincided with greater depression, anxiety or distress. The findings extend earlier evidence in two ways. First, previous reviews established that medical students experience high rates of depression, anxiety and distress [3-7], and that assessment is a particularly stressful component of medical education [2]. The present synthesis specifically quantifies the relationship between academic pressure and mental-health symptoms rather than simply re-estimating prevalence. A moderate correlation is meaningful at the population level because academic pressure is common and often recurrent; even a moderate average association can translate into a substantial institutional burden when most students are exposed to the relevant stressors repeatedly throughout training. Second, the repeated-measures and examination-proximal studies provide a useful quasi-temporal complement to the predominantly cross-sectional literature. In the Saudi cohort, depression, anxiety and stress all decreased when students moved from the pre-examination period to regular classes [9]. In the Tamil Nadu study, all three mood domains and salivary cortisol were higher on the examination day [17]. These patterns do not prove causation, but they are harder to explain solely by stable individual vulnerability. They suggest that academic context changes symptom expression, especially when assessment is imminent and perceived consequences are high. The large heterogeneity requires interpretation rather than dismissal. Academic pressure is not measured uniformly across countries or curricula. An MSSF learning-environment score, an ASS-40 classification, an MSSQ academic-related domain and a high-stakes examination are related but not identical exposures. Likewise, anxiety measured by GAD-7 is not psychometrically identical to DASS-21 anxiety or a Goldberg screening scale. Cultural expectations, grading systems, residency competition, financial pressure, clinical exposure and access to support services also differ. High I² therefore likely reflects genuine variation in constructs and educational environments in addition to sampling error. The consistency of positive effects across these different operationalizations is arguably as important as the exact pooled magnitude. Several mechanisms may connect academic pressure with depression and anxiety. Repeated high-stakes assessments can increase anticipatory worry, sleep disruption and rumination. Dense curricula reduce time available for recovery, physical activity and social support. Perceived failure or inability to master the syllabus may threaten professional identity and self-efficacy. The Chinese mediation study provides empirical support for a cognitive pathway in which rumination and ego depletion transmit part of the academic-stress effect to depressive mood [23]. The German longitudinal study similarly suggests that optimism and self-efficacy can modify the association between academic stress and depressive symptoms [15]. These findings fit the transactional view that stress depends not only on objective demands but also on appraisal, control and coping resources [8]. The results have practical implications for medical schools. Interventions should not be limited to telling students to become more resilient. Individual coping support is valuable, but academic pressure is partly generated by institutional design. Curriculum committees can map assessment density, reduce unnecessary clustering of major examinations, improve alignment between stated learning outcomes and tested material, increase formative feedback, and identify portions of the curriculum that create workload without proportionate educational value. Clearer expectations and transparent assessment criteria may reduce uncertainty-related anxiety. Protected recovery periods, early academic remediation, confidential counselling, peer support and rapid referral pathways should be integrated into the educational system rather than offered only after students reach crisis. Assessment reform deserves particular attention. The examination studies suggest that high-stakes periods are predictable windows of vulnerability. Institutions can use these windows for proactive support, including scheduled check-ins, flexible access to counselling, sleep and workload guidance, and review of examination timetables. At the same time, reducing academic pressure should not mean lowering educational standards. The goal is to distinguish productive challenge from avoidable overload. Well-designed medical education can remain rigorous while reducing redundant assessment, unclear expectations, excessive scheduling compression and punitive learning environments. This review has several limitations. Most included studies were cross-sectional, so reverse causation is possible: depressed or anxious students may perceive academic demands as more overwhelming. Unmeasured variables such as prior psychiatric history, personality, socioeconomic pressure, sleep, substance use and family expectations may confound observed associations. The number of studies reporting directly poolable effects was small, preventing robust subgroup meta-analysis, meta-regression or formal publication-bias testing. The secondary distress synthesis combined Pearson correlations with one standardized structural coefficient and should be treated as supportive rather than definitive. One included Nepalese study contained medical students and residents in the same broad trainee sample and therefore informed the narrative synthesis more than the pooled analyses [20]. Finally, this manuscript was prepared from a focused public-source search workspace. The strengths of the review include its focus on an actionable exposure rather than symptom prevalence alone, separation of statistically compatible and incompatible effect measures, explicit reporting of heterogeneity, and inclusion of recent 2025-2026 evidence. The consistency between meta-analytic correlations, adjusted regression findings, examination-period comparisons and mediation models supports a coherent overall interpretation: academic pressure is not merely a background feature of medical school but a measurable correlate of depression, anxiety and distress that can be targeted through educational and mental-health policy.

Academic pressure is positively and meaningfully related to depression, anxiety and psychological distress in medical students. Random-effects pooling showed moderate associations with depression (r=0.374) and anxiety (r=0.382), while a broader distress synthesis suggested a larger standardized relationship (approximately 0.52). Examination-proximal and curriculum-based studies reinforced these findings by showing symptom escalation during high-pressure periods and higher odds of adverse mental-health outcomes among students who struggled with academic demands. Medical schools should preserve academic rigor while reducing avoidable workload compression, improving assessment design and feedback, and embedding accessible psychological support into routine training. Future research should prioritize prospective multicenter cohorts, repeated measurements across assessment cycles, common academic-pressure instruments, and intervention studies capable of testing whether changes in educational design lead to measurable improvements in student mental health. Declarations Ethics approval and consent to participate: Not applicable. This study synthesized previously published data and did not involve direct recruitment of human participants.

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