The effect of economic downturn, financial hardship, unemployment, and relevant government responses on suicide
Bibliographic record
Abstract
Economic circumstances and related factors, including unemployment and poverty, can have substantial effects on suicide rates. This relationship applies in all countries, irrespective of their World Bank income status or level of development. Therefore, means of mitigating such influences are essential components of strategies to reduce suicides. In this Series paper, we consider examples of such initiatives, including national policies to try to reduce the effect of economic downturns, efforts to maintain employment and avoid damaging austerity measures, maintenance of reasonable minimum wage levels, and specific policies to assist those most affected by poverty. We also highlight upstream measures such as investment in transport infrastructure, industries, and retraining programmes. Positive public health messaging that encourages coping, together with discouragement of media stories with messages that could contribute to hopelessness in those experiencing economic difficulties, can also be important components of strategies to try to reduce the effect of economic downturn on suicide.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".