Injuries to Aboriginal populations living on- and off-reserve in metropolitan and non-metropolitan areas in British Columbia, Canada: Incidence and trends, 1986-2010
Bibliographic record
Abstract
BACKGROUND: Disparities in injury rates between Aboriginal and non-Aboriginal populations in British Columbia (BC) are well established. Information regarding the influence of residence on disparities is scarce. We sought to fill these gaps by examining hospitalization rates for all injuries, unintentional injuries and intentional injuries across 24 years among i) Aboriginal and total populations; ii) populations living in metropolitan and non-metropolitan areas; and iii) Aboriginal populations living on- and off-reserve. METHODS: We used data spanning 1986 through 2010 from BC's universal health care insurance plan, linked to vital statistics databases. Aboriginal people were identified by insurance premium group and birth and death record notations, and their residence was determined by postal code. "On-reserve" residence was established by postal code areas associated with an Indian reserve or settlement. Health Service Delivery Areas (HSDAs) were classified as "metropolitan" if they contained a population of at least 100,000 with a density of 400 or more people per square kilometre. We calculated the crude hospitalization incidence rate and the Standardized Relative Risk (SRR) of hospitalization due to injury standardizing by gender, 5-year age group, and HSDA. We assessed cumulative change in SRR over time as the relative change between the first and last years of the observation period. RESULTS: Aboriginal metropolitan populations living off-reserve had the lowest SRR of injury (2.0), but this was 2.3 times greater than the general British Columbia metropolitan population (0.86). For intentional injuries, Aboriginal populations living on-reserve in non-metropolitan areas were at 5.9 times greater risk than the total BC population. In general, the largest injury disparities were evident for Aboriginal non-metropolitan populations living on-reserve (SRR 3.0); 2.5 times greater than the general BC non-metropolitan population (1.2). Time trends indicated decreasing disparities, with Aboriginal non-metropolitan populations experiencing the largest declines in injury rates. CONCLUSIONS: Metropolitan/non-metropolitan residence appears to be a more important predictor than on/off-reserve residence for all injuries and unintentional injuries, and the relationship was even more pronounced for intentional injuries. The persistent disparities highlight the need for culturally sensitive and geographically relevant injury prevention approaches.
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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 | 0.002 |
| 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 | 0.000 |
| 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".