Age-related differences in work injuries: A multivariate, population-based study
Bibliographic record
Abstract
BACKGROUND: Many population-based studies find that the rate of work injuries is higher among adolescent and young adult workers compared to older adults. The present study examines age-related differences in work injuries, with an emphasis on adjusting for the potential confounding effects of job characteristics. METHODS: Age-related differences in work injuries were examined in a representative sample of 56,510 working Canadians aged 15 years and over. Respondents reported work-related injuries and job characteristics (e.g., occupation) in the past 12 months. Total hours worked in the past year were computed for each worker and accounted for in the logistic regressions. Analyses were stratified by gender. RESULTS: For men, adjusting for job characteristics substantially reduced, but did not eliminate the elevated risk status of adolescent and young adult workers. For women, only young adult women showed an elevated risk of work injury with job characteristics controlled. CONCLUSIONS: This is one of the few multivariate studies specifically examining contributors to age-related differences in work injuries in a population-based sample of workers. The substantial reduction in age-work injury association in the fully adjusted model suggests that differences in the types of jobs young workers hold play a critical role in their high-risk status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".