Injury Mortality Rates in Native and Non-Native Children: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVES: To examine injury mortality rates in Native and non-Native children in the province of Alberta, Canada, over a 10-year period, temporal trends in injury mortality rates (Native vs. non-Native), as well as relative risks of injury mortality (Native vs. non-Native) by injury mechanism and intent, were calculated. METHODS: An observational, population-based study design was used. Mortality data were obtained from provincial vital statistics, with injury deaths identified using external injury codes (E-codes). The relative risk (RR) of injury mortality (Native vs. non-Native) along with 95% confidence intervals (CIs) were calculated. Stratified analyses and Poisson regression modeling were used to calculate adjusted relative risk. RESULTS: Injury mortality rates declined over the study period, with no difference in the rate of decline between Native and non-Native children. The adjusted relative risk for all-cause injury death (Native vs. non-Native) was 4.6 (95% CI 4.1 to 5.2). The adjusted relative risks (Native vs. non-Native) by injury intent categories were: unintentional injuries, 4.0 (95% CI 3.5 to 4.6); suicide, 6.6 (95% CI 5.2 to 8.5); and homicide, 5.1 (95% CI 3.0 to 8.5). Injury mortality rates were consistently higher for Native children across all injury mechanism categories. The largest relative risks (Native vs. non-Native) were pedestrian injury (RR = 17.0), accidental poisoning (RR = 15.4), homicide by piercing objects (RR = 15.4), and suicide by hanging (RR = 13.5). CONCLUSION: The burden of injury mortality is significantly greater in Native children compared with non-Native children. Therefore, injury prevention strategies that target both intentional and unintentional injuries are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".