Improving police responses to suicide‐related emergencies: New evidence on the effectiveness of co‐response police‐mental health programs
Bibliographic record
Abstract
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 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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".