Childhood road traffic injuries in Canada – a provincial comparison of transport injury rates over time
Bibliographic record
Abstract
BACKGROUND: In Canada, road traffic injuries are the leading cause of death among children and youth ≤19. Across the country, there is variability in road traffic injury prevention policies and legislation. Our objective was to compare pediatric road traffic related injury hospitalization and death rates across Canadian provinces. METHODS: Population-based hospitalization and death rates per 100,000 were analyzed using data from the Discharge Abstract Database and provincial coroner's reports. Road traffic related injuries sustained by children and youth ≤19 years were analyzed by province and cause between 2006 and 2012. RESULTS: The overall transport-related injury morbidity rate for children in Canada was 70.91 per 100,000 population between 2006 and 2012. The Canadian population-based injury hospitalization rates from all transport-related causes significantly decreased from 85.51 to 58.77 per 100,000 (- 4.42; p < 0.01; - 5.42; - 3.41) during the study period. Saskatchewan had the highest overall transport related morbidity rate (135.69 per 100,000), and Ontario had the lowest (47.12 per 100,000). Similar trends were observed for mortality rates in Canada. CONCLUSIONS: Transport-related injuries among children and youth have significantly decreased in Canada from 2006 to 2012; however the rates vary by province and cause.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".