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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".