Injury Mortality Rates in Native and Non-Native Children: A Population-Based Study
Why this work is in the frame
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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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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 | 0.012 | 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 it