Claim Characteristics and Return to Work Outcomes for Ontario Public Safety Personnel with Mental Stress Injury Program Claims, 2014–2023
Bibliographic record
Abstract
PURPOSE: In Canada, rates of psychological injury for public safety personnel (PSP), along with related workers' compensation costs, have been on the rise in the past decade. This study explored approved workers' compensation claims filed by PSP for work-related psychological injuries through the Ontario Workplace Safety and Insurance Board (WSIB) between 2014 and 2023. Specifically, we wanted to understand the variability in demographic and claim characteristics and how return to work (RTW) outcomes compared amongst PSP occupations. METHODS: This research employed a descriptive and quantitative analysis to identify trends in claim volumes, injury categories, and patterns in RTW outcomes for communicators, correctional workers, firefighters, paramedics, and police. RESULTS: Claimants were more often male with an average age of 41.3 years and 13 years of work experience at the time of injury. Police and paramedics accounted for over 60% of all claims and significant heterogeneity was observed across all occupations. Cumulative traumatic injury claims were more common than single event claims, and PTSD was the most common category of claim. 93.3% of all claims resulted in time lost from work, the median claim length was 14.4 months (Q1 = 0.8, Q3 = 38.7), and only 35.7% of claimants had a successful RTW outcome documented. The most favorable profile for RTW success was for younger and less experienced workers, with single event or traumatic mental stress claims. CONCLUSION: Our findings can inform the development of more effective public policies and workers' compensation processes, ultimately contributing to more timely and effective support for PSP who sustain work-related psychological injuries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".