Financial Stress, Unemployment, and Suicide – A Meta-Analysis
Bibliographic record
Abstract
Abstract: Background: Socioeconomic factors such as financial stress and unemployment are known predictors of suicide. However, no large-scale meta-analyses exist. Aims: Determine the suicide risk following unemployment or financial stress. Method: Literature searched through July 31, 2021. Robust meta-analysis and metaregression of the risk of suicide following financial stress (23 studies) or unemployment (43 studies), from 20 nations. Subgroup meta-analyses by sex, age, year, country, and methodology. Results: The suicide risk following financial stress or unemployment was not significantly elevated among those with diagnosed mental illness. In the general population, we found significantly elevated suicide risks for financial stress (RR: 1.742; 95% CI: 1.339, −2.266) and unemployment (RR: 1.874; CI: 1.501, −2.341). However, neither was significant among studies controlling for physical/mental health (perhaps partially due to lower statistical power). We observed no significant differences by sex, age, or by GDP. We observed a higher suicide risk following unemployment in more recent years. Limitations: Publication bias was evident. We could not examine some individual-level characteristics, most notably the severity/duration of unemployment/financial stress. Heterogeneity was high for some meta-analyses. Studies from non-OECD countries are under-represented. Conclusion: After accounting for physical/mental health, financial stress and unemployment weakly associated with suicide, and the associations may be nonsignificant.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".