Occupational therapy treatment of public safety personnel with work-related psychological injuries: analyzing Ontario worker’s compensation data from 2017–2021
Bibliographic record
Abstract
This study explored the usage of occupational therapy treatment with psychologically injured public safety personnel (PSP) from Ontario, Canada. We used a descriptive quantitative approach with summary data provided by the Workplace Safety and Insurance Board (WSIB) of Ontario documenting occupation therapy (OT) treatment of psychologically injured PSP who had an approved WSIB Mental Stress Injury Program (MSIP) claim between 2017 and 2021. Variables examined included demographics, career type, injury type, and return to work (RTW) outcomes. Chi-square Tests of Independence were used to compare differences between PSP who received OT treatment and those who did not. Analysis revealed that in the total cohort of 6674 approved PSP MSIP claims, 15% (n = 991) of PSP received OT treatment. Communicators (21%) and correctional workers (17%) were most likely to receive OT treatment while paramedics (13%) were less likely. PSP claimants who received OT treatment were more likely to have a cumulative event injury (71%) compared to the rest of the cohort (55%) and were more likely to not have started a RTW process (62%) compared to the rest of the cohort (43%). PSP who received OT treatment had more days away from work on average than those who did not (913 days vs. 384 days). This data reveals that PSP with cumulative injuries and higher lengths of time away from work more frequently received OT treatment as part of their WSIB MSIP claim; it is possible that this higher degree of claim complexity influenced their RTW outcomes. Worker's compensation organizations should consider their health care decision-making processes to foster prompt access to treatment and proactive RTW pathways.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".