Intentional injury among the indigenous and total populations in British Columbia, Canada: trends over time and ecological analyses of risk
Bibliographic record
Abstract
Our objective was to explore intentional injury disparity between Indigenous populations and the total population in the province of British Columbia (BC), Canada. We focus on hospitalizations, including both self-inflicted injuries and injuries inflicted by others. We used data from BC’s universal health care insurance plan, 1991 to 2010, linked to Vital Statistics databases. Indigenous people were identified through the insurance premium group, and birth and death records. Place of residence was identified through postal code. We calculated crude hospitalization incidence rates and the Standardized Relative Risk (SRR) of hospitalization, standardized by gender, 5-year age group, and Health Service Delivery Area (HSDA). With HSDA populations as the units of observation, linear regression was used to test hypothesized associations of Indigenous ethnicity, geographic, and socio-economic characteristics with SRR of injury. During the period 1991–2010, the crude rate of hospitalization for intentional injuries was 8.4 per 10,000 person-years (95% confidence interval (CI): 8.3 to 8.5) for the total BC population, compared to 45.3 per 10,000 (95% CI: 44.5 to 46.1) for the Indigenous population. For both populations, risk declined over the period for injuries self-inflicted and inflicted by others. The linear regression model predicts that the off-reserve Indigenous population will have SRR of intentional injury 3.98 greater, and the on-reserve Indigenous population 4.17, greater than the total population. The final model was an excellent fit (R 2 = 0.912, F = 177.632, p < 0.001), and found that three variables - occupational risk, high school diploma, and university degree – each provide independent effects when interacting multiplicatively with Indigenous ethnicity. The observation of substantially declining rates of intentional injury for both the Indigenous and total BC populations is off-set by the high disparity in risk between the two populations, which will likely continue until Canada reduces disparity with respect to discriminatory practices, and physical, social, and economic conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 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".