Long-Term Effects of a Comprehensive Police Suicide Prevention Program
Bibliographic record
Abstract
Abstract. Background: Mishara and Martin (2012) reported decreases in suicides 12 years after implementation of a police suicide prevention program. Aims: We aimed to determine whether suicide decreases were sustained 10 years later. Method: We examined coroners’ investigations of police deaths from 2009 through 2018. Results: From 2009 to 2018, Montreal suicide rates increased but this was not significantly different from the previous 12 years and the rate for other Quebec police remained significantly higher than Montreal ( p < .006). The 22-year Montreal postprogram rate was significantly lower than the preprogram rate ( p < .002), and the 22-year rate for other police during the same years was not significantly different from earlier. Limitations: Uncontrolled factors may have influenced the rates, including the 11% increase in women in the Montreal police. The observed mean aging of the Montreal police personnel would have been expected to bias toward finding increases in suicides. However, the maintenance of decreases in suicide rates was observed. Conclusion: The decrease in suicides observed 12 years after the program was sustained for another 10 years, and appears related to the program. Rates for comparable police remained higher. A continuing comprehensive suicide prevention program tailored to the context may reduce suicides for extended time periods.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".