A Comparison of Suicides in Public Safety Personnel With Suicides in the General Population in Ontario, 2014 to 2018
Bibliographic record
Abstract
Abstract: Background: There is conflicting evidence on the suicide rates of different public safety personnel (PSP). There have been few studies that compare suicides in PSP with the general population and none that have used a detailed comparison of coroner records. Aims: The current study estimates suicide rates among different PSP and compares PSP suicides with the general population. Method: We identified coroner records of PSP suicides from January 2014 to December 2018 and compared each one to two matched general population controls. Results: We identified 36 PSP suicides and 72 general population controls. Police had a higher suicide rate than other PSP groups. PSP were more likely to die by firearm, be separated/divorced or married, die in a motor vehicle, have problems at work, and have a PTSD diagnosis. PSP were less likely to die by jumping. Limitations: The study may have not identified all PSP suicides. Apart from the cause of death, data in coroner records are not systematically collected, so information may be incomplete. Conclusion: PSP suicides appear different than the general population. Death records need to have an occupation identifier to enable monitoring of trends in occupational groups, such as PSP.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".