Factors associated with working status among workers assessed at a specialized worker's compensation board psychological trauma program
Bibliographic record
Abstract
BACKGROUND: Psychological morbidity following trauma occurring in the workplace can impact return to work but few studies have investigated this. METHODS: This study was a secondary analysis of administrative data from a specialized workers' compensation board psychological trauma program in Toronto, Canada. Unadjusted and adjusted logistic regression analyses were used to examine factors associated with working status at the time of assessment for workers referred within 1 year of traumatic event. RESULTS: Having a disrupted marriage (OR = 3.06, 95% CI 1.14-8.20), sustaining a permanently impairing physical injury (OR = 2.76, 95% CI 1.01-7.55) and the presence of secondary psychiatric diagnoses (OR = 2.55, 95% CI 1.34-4.83) were significantly associated with not working at the time of assessment. When the analyses were subset to workers without permanently impairing physical injuries, only the presence of additional psychiatric diagnoses was significantly associated with not working (OR = 3.81, 95% CI 1.48-9.83). CONCLUSIONS: Return to work after trauma can be a complicated and difficult to treat problem. Social supports, physical rehabilitation and treatment of complex mental health problems likely play a crucial role in improving outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".