Outcomes of Community‐Based Suicide Prevention Approaches That Involve Reducing Access to Pesticides: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: Pesticide ingestion is among the most commonly utilized means of suicide worldwide. Restricting access to pesticides at a local level is one strategy to address this major public health problem, but little is known about its effectiveness. We therefore conducted a systematic literature review to identify effective community-based suicide prevention approaches that involve restricting access to pesticides. METHOD: We searched Embase, Scopus, PsycINFO, Cochrane Library, CINAHL, and PubMed for well-designed studies that reported on suicide-related outcomes (i.e., attempted or completed suicide). RESULTS: We identified only five studies that met our eligibility criteria (two randomized controlled trials, two studies with quasi-experimental designs, and one study with a before-and-after design). These studies tested different interventions: the introduction of nonpesticide agricultural management, providing central storage facilities for pesticides, distributing locked storage containers to households, and local insecticide bans. The only sufficiently powered study produced no evidence of the effectiveness of providing household storage containers. Three interventions showed some promise in reducing pesticide suicides or attempts, with certain caveats. CONCLUSIONS: Our review identified three community interventions that show some promise for reducing pesticide suicides by restricting access to means, which will require replication in large, well-designed trials before they can be recommended.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.011 |
| 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) | 0.005 | 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".