Nonwage Losses Associated With Occupational Injury Among Health Care Workers
Bibliographic record
Abstract
OBJECTIVE: To examine nonwage losses after occupational injury among health care workers and the factors associated with the magnitude of these losses. METHODS: Inception cohort of workers filing an occupational injury claim in a Canadian province. Worker self-reports were used to calculate (1) the nonwage economic losses in 2010 Canadian dollars, and (2) the number of quality-adjusted days of life lost on the basis of the EuroQOL Index. RESULTS: Most workers (84%; n = 123) had musculoskeletal injuries (MSIs). Each MSI resulted in nonwage economic losses of Can$3131 (95% confidence interval, Can$3035 to Can$3226), lost wages of Can$5286, and 7.9 quality-adjusted days of life lost within 12 weeks after injury. Losses varied with type of injury, region of the province, and occupation. Non-MSIs were associated with smaller losses. CONCLUSIONS: These estimates of nonwage losses should be considered in workers' injury compensation policies and in economic evaluation studies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".