Suicidal Behaviors Among Medical Students: A Scoping Review of Systematic Reviews and Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Suicidal ideation and attempts are major public health concerns among young adults, particularly those in demanding academic settings. Medical students exhibit disproportionately high rates compared to peers in the general population and other fields of study, highlighting the urgent need to understand and address mental health challenges in medical education. OBJECTIVE: This scoping review summarizes evidence from systematic reviews and meta-analyses on the prevalence and risk factors of suicidal ideation and suicide attempts among medical students worldwide. METHODS: Following PRISMA-ScR guidelines, six databases were searched for peer-reviewed reviews published in the last ten years. Studies focused exclusively on medical students and reporting prevalence or risk factors of suicidal ideation or attempts were included. Data were charted on prevalence, risk factors, study characteristics, and recommendations. RESULTS: Twelve reviews comprising 378,081 medical students were included. Lifetime prevalence of suicidal ideation ranged from 2.9% to 53.6% among the systematic reviews, with pooled estimates from meta-analyses ranging from 11% and 25%. Attempted suicide pooled prevalences ranged from 1.64% to 8%. Depression was frequently reported as the most significant risk factor for both suicidal ideation and attempts. Other significant risk factors for suicidal ideation included anxiety, burnout, female gender, financial strain, and academic stress. Suicidal ideation was higher during the COVID-19 pandemic and among clinical-phase students. Gender differences in suicide attempts were inconsistent. Medical students' rates of suicidal behavior exceeded those of other university students. CONCLUSION: Suicidal behavior remains a critical mental health issue for medical students globally. Despite known risk factors, targeted interventions are limited. Future research should emphasize longitudinal studies, post-pandemic effects, regional gaps, and intervention development. Implications are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".