Education and suicidal ideation in Europe: A systematic review and meta-analysis
Bibliographic record
Abstract
Understanding predictors of suicidal ideation (SI) is crucial for preventing suicides. Given Europe's high suicide rates and the complex nature of SI, it is essential to also examine social determinants like education as potential risk factors for SI in this region. This systematic review and meta-analysis investigates the association between formal/vocational education and SI in Europe. Electronic databases (PubMed, Web of Science, PsycINFO, PSYNDEX) were searched until November 2022. Included studies involved European populations examining associations between education and SI. Pooled Odds Ratios (OR) with 95 % confidence intervals (CI) were calculated using random-effects models. Heterogeneity was assessed with the heterogeneity variance τ2 and I2 statistic; subgroup analyses were performed based on study characteristics. Risk of bias was assessed using an adaption of the Newcastle-Ottawa Scale. From 20,564 initial studies, 41 were included in the meta-analysis (outlier-adjusted, 96,809 study participants). A negative, insignificant association (OR = 0.86, 95 % CI: 0.75; 1.00) was observed between education and SI, with significant heterogeneity (τ2 = 0.09, I2 = 73 %). Subgroup analyses indicated that population type, age group, categorization of education, timeframe of SI assessment, and study quality significantly moderated the effect size. Heterogeneity across studies limits generalizability. The cross-sectional design precludes establishing causal relationships, and social desirability bias may have underestimated the association between education and SI. This systematic review and meta-analysis suggests a trend towards a protective effect of education on the emergence of SI in Europe. Future research, preferably with longitudinal study design examining various covariates, should systematically consider educational inequalities in SI.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".