Mental Health Secretariat: Collaboration for public safety personnel (PSP) mental health in Ontario
Bibliographic record
Abstract
Mental health issues, and more specifically suicide, within the policing community have been a growing concern in recent years. In 2018 alone, there were nine suicides among active and retired police officers in the province of Ontario. At the time, nine suicides in one year were shocking and began to raise focused awareness of mental health challenges facing the profession. In 2021, the Ontario Ministry of the Solicitor General created Mental Health Collaborative Tables comprised of key stakeholders, subject matter experts, public safety personnel (PSP) with lived experience, mental health clinicians, and researchers. The Mental Health Secretariat (MHS) is responsible for supporting the tables. The MHS is accountable to the Deputy Solicitor General and has a mandate to provide a provincial action plan to address mental health issues among PSP. This article explains key observations regarding Ontario’s innovative approach to improving mental health supports for PSP and describes the perspective offered by Karen Prokopec, Manager, MHS at Ontario Ministry of the Solicitor General, and her colleague, Zarsanga Popal, Senior Performance Measurement and Evaluation Specialist with the MHS, on the establishment of the MHS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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".