Primary care visits due to injuries among the Aboriginal off-reserve population of British Columbia, Canada, 1991–2010
Bibliographic record
Abstract
BACKGROUND: Aboriginal people in British Columbia (BC) have higher injury incidence than the general population. This report describes variability in visits to primary care due to injury, among injury categories, time periods, geographies, and demographic groups. METHODS: We used BC's universal health care insurance plan as a population registry, linked to practitioner payment and vital statistics databases. We identified Aboriginal people by insurance premium group and birth and death record notations. Within that population we identified those residing off-reserve according to postal code. We calculated crude incidence and Standardized Relative Risk (SRR) of primary care visit due to injury, standardized for age, gender and Health Service Delivery Area (HSDA), relative to the total population of BC. RESULTS: During 1991 through 2010, the crude rate of primary care visit due to injury in BC was 3172 per 10,000 person-years. The Aboriginal off-reserve rate was 4291 per 10,000 and SRR was 1.41 (95 % confidence interval: 1.41 to 1.42). Northern and non-metropolitan HSDAs had higher SRRs, within both total BC and Aboriginal off-reserve populations. In every age and gender category, the HSDA-standardized SRR was higher among the Aboriginal off-reserve than among the total population. For all injuries combined, and for the categories of trauma, poisoning, and burn, between 1991 and 2010, crude rates and SRRs declined substantially, but proportionally more rapidly among the Aboriginal off-reserve population, so the gap between the Aboriginal off-reserve and total populations is narrowing, particularly among metropolitan residents. CONCLUSIONS: These findings corroborate our previous reports regarding hospitalizations due to injury, suggesting that our observations reflect real disparities and changes in the underlying incidence of injury, and are not merely artefacts related to health care utilization.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".