Descriptive Epidemiology of Serious Work-Related Injuries in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: This study examined the rates and distribution of serious work-related injuries by demographic, work and injury characteristics in British Columbia, Canada from 2002-2008, using population-based data. METHODS: Claims for workers with a serious injury were extracted from workers' compensation data. Serious injuries were defined by long duration, high cost, serious medical diagnosis, or fatality. Workforce estimates were used to calculate stratum-specific rates. Rate-ratios (RR) and 95% CIs were calculated using negative binomial regression for the comparison of rates, adjusting for gender, age and occupation. RESULTS: Women had a lower overall serious injury rate compared to men (RR: 0.93, 95% CI: 0.87-0.99). The 35-44 age group had the highest overall rate compared to the youngest age group. The rate for severe strains/sprains was similarly high for men and women in the 35-44 age group, although there was a differential pattern by gender for other injury types: the rate of fracture was similar across age groups for men, but increased with age for women (RR: 2.7, 95% CI: 2.2-3.3); and the rate of severe falls increased with age for men and women, with a larger three-fold increase for older women (men: RR: 1.8, 95% CI: 1.7-2.1; women: RR: 3.2, 95% CI: 2.7-3.7). CONCLUSIONS: The risk of serious injuries is higher among specific age groups with different patterns emerging for men and women. Variations persisted within similar injury types and occupation groups in our adjusted models. These results provide evidence for the burden of serious injuries and a basis for future analytic research. Given projected demographic shifts and increasing workforce participation of older workers, intervention programs should be carefully implemented with consideration to demographic groups at risk for serious injuries in the workplace.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".