Utilization and Impact of Peer-Support Programs on Police Officers’ Mental Health
Bibliographic record
Abstract
Police officer suicide rates hit an all-time high in the province of Ontario, Canada, in 2018. Sadly, this statistic is somewhat unsurprising, as research has shown that police officers suffer from higher rates of mental health disorder diagnoses compared to the general public. One key reason for the elevated levels of suicide and other mental health issues among police officers is believed to stem from the stigma associated with seeking help. In an attempt to address these serious issues, Ontario's police services have begun to create internal peer support programs as a way of supporting their members. The present research explores the experiences of police officers serving as peer-support team members, particularly with regards to the impacts of peer support. In addition, this research also examines the importance of discussing shared experiences regarding a lack of standardized procedures for the administration and implementation of peer support in relation to the Policy Feedback Theory. The Policy Feedback Theory (PFT) posits that, when a policy becomes established and resources are devoted to programs, it helps structure current activity. This study utilized a phenomenological, qualitative approach, with data collection consisting of face-to-face interviews with nine police officers serving on the York Regional Police's peer-support team. The findings revealed that peer support is more than just a "conversation"; rather, it suggests to contribute to enhancing mental health literacy among police officers, and it significantly contributes to stigma reduction. The findings also revealed that internal policy demonstrated an organizational commitment to mental health and peer-support, and that a provincial standard is necessary to ensure best practices and risk management in the creation and maintenance of peer-support programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".