Worker compensation injuries among the Aboriginal population of British Columbia, Canada: incidence, annual trends, and ecological analysis of risk markers, 1987–2010
Bibliographic record
Abstract
BACKGROUND: Aboriginal people in British Columbia (BC) have higher injury incidence than the general population, but information is scarce regarding variability among injury categories, time periods, and geographic, demographic and socio-economic groups. Our project helps fill these gaps. This report focuses on workplace injuries. METHODS: We used BC's universal health care insurance plan as a population registry, linked to worker compensation and vital statistics databases. We identified Aboriginal people by insurance premium group and birth and death record notations. We identified residents of specific Aboriginal communities by postal code. We calculated crude incidence rate and Standardized Relative Risk (SRR) of worker compensation injury, adjusted for age, gender and Health Service Delivery Area (HSDA), relative to the total population of BC. We assessed annual trend by regressing SRR as a linear function of year. We tested hypothesized associations of geographic, socio-economic, and employment-related characteristics of Aboriginal communities with community SRR of injury by multivariable linear regression. RESULTS: During the period 1987-2010, the crude rate of worker compensation injury in BC was 146.6 per 10,000 person-years (95% confidence interval: 146.4 to 146.9 per 10,000). The Aboriginal rate was 115.6 per 10,000 (95% CI: 114.4 to 116.8 per 10,000) and SRR was 0.88 (95% CI: 0.87 to 0.89). Among those living on reserves SRR was 0.79 (95% CI: 0.78 to 0.80). HSDA SRRs were highly variable, within both total and Aboriginal populations. Aboriginal males under 35 and females under 40 years of age had lower SRRs, but older Aboriginal females had higher SRRs. SRRs are declining, but more slowly for the Aboriginal population. The Aboriginal population was initially at lower risk than the total population, but parity was reached in 2006. These community characteristics independently predicted injury risk: crowded housing, proportion of population who identified as Aboriginal, and interactions between employment rate and income, occupational risk, proportion of university-educated persons, and year. CONCLUSIONS: As employment rates rise, so has risk of workplace injury among the Aboriginal population. We need culturally sensitive prevention programs, targeting regions and industries where Aboriginal workers are concentrated and demographic groups that are at higher risk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".