The prevalence of suicidal thoughts and behaviours among college students: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Adolescence and young adulthood carry risk for suicidal thoughts and behaviours (STB). An increasing subpopulation of young people consists of college students. STB prevalence estimates among college students vary widely, precluding a validated point of reference. In addition, little is known on predictors for between-study heterogeneity in STB prevalence. METHODS: A systematic literature search identified 36 college student samples that were assessed for STB outcomes, representing a total of 634 662 students [median sample size = 2082 (IQR 353-5200); median response rate = 74% (IQR 37-89%)]. We used random-effects meta-analyses to obtain pooled STB prevalence estimates, and multivariate meta-regression models to identify predictors of between-study heterogeneity. RESULTS: Pooled prevalence estimates of lifetime suicidal ideation, plans, and attempts were 22.3% [95% confidence interval (CI) 19.5-25.3%], 6.1% (95% CI 4.8-7.7%), and 3.2% (95% CI 2.2-4.5%), respectively. For 12-month prevalence, this was 10.6% (95% CI 9.1-12.3%), 3.0% (95% CI 2.1-4.0%), and 1.2% (95% CI 0.8-1.6%), respectively. Measures of heterogeneity were high for all outcomes (I 2 = 93.2-99.9%), indicating substantial between-study heterogeneity not due to sampling error. Pooled estimates were generally higher for females, as compared with males (risk ratios in the range 1.12-1.67). Higher STB estimates were also found in samples with lower response rates, when using broad definitions of suicidality, and in samples from Asia. CONCLUSIONS: Based on the currently available evidence, STB seem to be common among college students. Future studies should: (1) incorporate refusal conversion strategies to obtain adequate response rates, and (2) use more fine-grained measures to assess suicidal ideation.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.058 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".