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Record W4409321383 · doi:10.1101/2025.04.09.25325515

A rapid review of interventions to reduce suicide ideation, attempts, and deaths at public locations

2025· review· en· W4409321383 on OpenAlexaboutno aff
Meg Kiseleva, Juliet Hounsome, Mala Mann, Abubakar Sha’aban, Riya Reji, Jacob Davies, Rhiannon Tudor Edwards, Adrian Edwards, Alison Cooper, Ruth Lewis

Bibliographic record

VenuemedRxiv · 2025
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
FundersHealth and Care Research Wales
KeywordsPsychological interventionIdeationSuicide ideationSuicidal ideationPsychologySuicide preventionEnvironmental healthMedical emergencyPolitical scienceMedicinePsychiatryPoison control

Abstract

fetched live from OpenAlex

Abstract Suicide deaths are tragic events and those that occur in public places have an impact not only on the deceased person and their family and friends, but also on members of the public. Having up-to-date information about the effectiveness of interventions allows policymakers and organisations managing locations of concern to choose the most appropriate evidence-based suicide prevention strategies for specific locations. This rapid review was conducted to help inform the development of Welsh national guidance. The review included literature published since 2014. 24 studies were identified, and these were conducted in the UK, Australia, South Korea, Canada, USA, Denmark and Japan. The studies covered railway or underground stations, bridges, cliffs or other natural heights, tall buildings, and other types of locations. Surveillance technologies as a means of increasing opportunity for third-party intervention showed the most promise, although the evidence of their effectiveness was limited. In one study, having more closed-circuit television (CCTV) units was associated with fewer suicides at railway stations. Another study that tested a set of interventions including CCTV, infrared security fences, and a suicidal behaviour recognition and alert system, provided some promising initial descriptive data that showed an increase in the number of prevented suicides. Three other studies showed that there was no change in outcomes following the installation of interventions including surveillance technologies. Based on the assessment of the overall body of the evidence, there is a low level of confidence in the findings related to surveillance technologies because of the quality and designs of the studies. Promotion of suicide helplines as an intervention aimed at increasing opportunities for help seeking was examined in seven studies. Two studies reported that the number of suicides increased after the introduction of the intervention. Three studies, of which two examined a set of interventions including helplines, observed no change. In two studies the effect could not be determined. There is a low level of confidence in the evidence for this outcome. Other interventions evaluated included staff training; deployment of specialist staff; campaigns encouraging bystanders to intervene; a crisis café; blue lights at railway stations; suicide prevention messages, memorials, or notes other than official crisis line signage; spinning rollers at the top of fences that prevent gripping; and others. The effect of these interventions could not be determined with certainty but some of them appeared promising and warrant further research. More robust evaluations are needed before any of the interventions reviewed here can be recommended for implementation. To create a better evidence base, high-quality evaluations should be supported and encouraged. Future research should examine which interventions work for who and in what circumstances.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.045
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0150.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.165
GPT teacher head0.435
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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