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Record W3177949317 · doi:10.1111/sltb.12792

Improving police responses to suicide‐related emergencies: New evidence on the effectiveness of co‐response police‐mental health programs

2021· article· en· W3177949317 on OpenAlexaffabout
Étienne Blais, David Brisebois

Bibliographic record

VenueSuicide and Life-Threatening Behavior · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMental healthPsychological interventionPsychosocialPropensity score matchingEmergency responsePsychologyMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Several police organizations have implemented training programs and co-response police-mental health programs to improve interventions among people in crisis. Some researchers have questioned the "one size fits all" approach of these programs and their ability to improve the management of specific psychosocial emergencies such as suicide-related behaviors. OBJECTIVES: This study evaluates the effect of a co-response police-mental health program introduced by the Laval Police Department to improve interventions in suicide-related calls. METHODS: Propensity score matching techniques were used to match 130 observations of a control group with 251 observations of a treatment group. Average treatment effects (ATEs) were then computed. RESULTS: Results indicate that the co-response program was associated with significant decreases in police use of force (ATE = -0.077; p ≤ 0.05) and transports to hospital (ATE = -0.773; p ≤ .01). Increases were observed in referrals to community resources (ATE = 0.285; p ≤ 0.01), and individuals managed through their social network (ATE = 0.530; p ≤ 0.01). CONCLUSION: The findings suggest that co-response police-mental health programs can improve the management of people showing suicide-related behaviors.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.395
Teacher spread0.306 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations29
Published2021
Admission routes2
Has abstractyes

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