Impact of interventions at frequently used suicide locations on occurrence of suicides at other sites: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Interventions at frequently used suicide locations that restrict access to means, encourage help-seeking, and increase the likelihood of intervention by a third party are effective in preventing suicide at such sites. However, there have been concerns that such efforts may displace suicides to other sites. It is important to synthesize the evidence on suicide displacement effects. METHODS: We conducted a systematic search of Medline, PsycINFO, Scopus, and Google for eligible studies from their inception to February 20, 2025. Meta-analyses were conducted to assess the pooled effects of interventions on suicides at frequently used locations and other sites, and on overall suicides involving the same method. RESULTS: Our search identified 17 studies. Meta-analyses showed a reduction in suicides at the intervention sites (pooled incidence rate ratio [IRR] 0.09, 95% confidence interval [95% CI] 0.04-0.21) and no evidence of changes in suicides at other sites after restricting access to means was deployed alone. The pooled IRR for nearby sites (same type) was 0.99 (95% CI 0.72-1.38); for other sites (same type), it was 0.99 (95% CI 0.76-1.29); and for other sites (different/unspecified type), it was 1.19 (95% CI 0.90-1.58). There was an overall reduction in suicides involving the same method during the post-intervention period (IRR 0.77, 95% CI 0.65-0.92). Similar patterns were observed when restricting access to means was assessed alone or with other interventions. CONCLUSIONS: Suicide numbers at other sites did not change after interventions such as restricting access to means were deployed at frequently used locations.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.053 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".