Injury risk in British Columbia, Canada, 1986 to 2009: are Aboriginal children and youth over-represented?
Bibliographic record
Abstract
BACKGROUND: Children and youth worldwide are at high risk of injury resulting in morbidity, disability or mortality. Disparities in risk exist between and within countries, and by sex and ethnicity. Our aim is to contribute data on disparities of injury rates for Aboriginal children and youth compared with those of the general population in British Columbia (BC), Canada, by examining risks for the two populations, utilizing provincial administrative data over a 24-year period. METHODS: Hospital discharge records from the provincial health care database for children and youth were used to identify injury for the years 1986 to 2009. Within the total BC population, the Aboriginal population was identified. Crude rates and standardized relative risks (SRR) of hospitalization were calculated, by year and category of injury type and external cause, and compared to the total BC population for males and females under age 25 years. RESULTS: Over the 24-year period, substantive decreases were found in hospitalization injury risks for children and youth in both Aboriginal and total populations, for both sexes, and for most categories and types of injuries. Risk in overall injury dropped by 69% for the Aboriginal population and by 66% for the total BC population, yet in every year, the Aboriginal population had a higher risk than the total BC population. There were over 70% declines in risks among females of intentionally inflicted injury by another, among both the Aboriginal and total BC populations. Risk of injury caused by transport vehicles has decreased by an overwhelming 83% and 72% for the Aboriginal male population and for the total BC male population, respectively. CONCLUSIONS: The over 70% declines in risks for females of intentionally inflicted injury by another, among both the Aboriginal and total BC populations is excellent news. Risk of injury caused by transport vehicles for males decreased overwhelmingly for both populations. Disparities in rates between the Aboriginal population and total BC population remain because of similarity in the proportional reductions among the two populations. Since the Aboriginal population started at a much higher risk, in absolute terms, the gap between the two populations is shrinking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.001 | 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".