Risk factors for suicidal ideation and suicide attempt among medical students: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Medical training poses significant challenge to medical student wellbeing. With the alarming trend of trainee burnout, mental illness, and suicide, previous studies have reported potential risk factors associated with suicidal behaviours among medical students. The objective of this study is to provide a systematic overview of risk factors for suicidal ideation (SI) and suicide attempt (SA) among medical students and summarize the overall risk associated with each risk factor using a meta-analytic approach. METHODS: Systemic search of six electronic databases including MEDLINE, Embase, Education Source, Scopus, PsycInfo, and CINAHL was performed from database inception to March 19, 2021. Studies reporting original quantitative or epidemiological data on risk factors associated with SI and SA among undergraduate medical students were included. When two or more studies reported outcome on the same risk factor, a random-effects inverse variance meta-analysis was performed to estimate the overall effect size. RESULTS: Of 4,053 articles identified, 25 studies were included. Twenty-two studies reported outcomes on SI risk factors only, and three studies on both SI and SA risk factors. Meta-analysis was performed on 25 SI risk factors and 4 SA risk factors. Poor mental health outcomes including depression (OR 6.87; 95% CI [4.80-9.82] for SI; OR 9.34 [4.18-20.90] for SA), burnout (OR 6.29 [2.05-19.30] for SI), comorbid mental illness (OR 5.08 [2.81-9.18] for SI), and stress (OR 3.72 [1.39-9.94] for SI) presented the strongest risk for SI and SA among medical students. Conversely, smoking cigarette (OR 1.92 [0.94-3.92]), family history of mental illness (OR 1.79 [0.86-3.74]) and suicidal behaviour (OR 1.38 [0.80-2.39]) were not significant risk factors for SI, while stress (OR 3.25 [0.59-17.90]), female (OR 3.20 [0.95-10.81]), and alcohol use (OR 1.41 [0.64-3.09]) were not significant risk factors for SA among medical students. CONCLUSIONS: Medical students face a number of personal, environmental, and academic challenges that may put them at risk for SI and SA. Additional research on individual risk factors is needed to construct effective suicide prevention programs in medical school.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.059 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".