Worker's compensation usage and return to work outcomes for Ontario public safety personnel with mental stress injury claims: 2017–2021
Bibliographic record
Abstract
This study explored approved worker's compensation claims made by public safety personnel (PSP) with work-related psychological injuries to the Workplace Safety and Insurance Board (WSIB) of Ontario's Mental Stress Injury Program (MSIP) between 2017 and 2021. This worker's compensation program provides access to health care coverage, loss of earnings benefits, and return to work support services for psychologically injured workers. In 2016, the Government of Ontario amended legislation to presume that, for this population, posttraumatic stress disorder (PTSD) is work-related, potentially expanding access to the program. The aim of this study was to understand the volume and types of claims, return to work rates, and differences between PSP career categories in the first 5 years after the legislative change. Using a quantitative descriptive approach, statistical analysis revealed that claims increased over the 5-year period, with significantly more claims made in 2021 (n = 1,420) compared to 2017 (n = 1,050). Of the 6,674 approved claims, 33.5% were made by police, 28.4% by paramedics, 21.6% by correctional workers, 9.4% by firefighters, and 7.1% by communicators. Analysis of claim type revealed that police, firefighters, and communicators made more cumulative incident claims, while paramedics made more single incident claims. Differences were also observed in return to work rates, with fewer police officers, firefighters, and communicators assigned to a return to work program, and more paramedics successfully completing a return to work program. This study sheds light on differences among PSP in their WSIB Ontario MSIP claims and underscores the importance of continued research to develop a more robust understanding of these differences, to inform policy development for both employers and worker's compensation organizations.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".