Canadian Child Safety Report Card: a comparison of injury prevention practices across provinces
Bibliographic record
Abstract
BACKGROUND: Injury prevention report cards that raise awareness about the preventability of childhood injuries have been published by the European Child Safety Alliance and the WHO. These report cards highlight the variance in injury prevention practices around the world. Policymakers and stakeholders have identified research evidence as an important enabler to the enactment of injury legislation. In Canada, there is currently no childhood injury report card that ranks provinces on injury rates or evidence-based prevention policies. METHODS: Three key measures, with five metrics, were used to compare provinces on childhood injury prevention rates and strategies, including morbidity, mortality and policy indicators over time (2006-2012). Nine provinces were ranked on five metrics: (1) population-based hospitalisation rate/100 000; (2) per cent change in hospitalisation rate/100 000; (3) population-based mortality rate/100 000; (4) per cent change in mortality rate/100 000; (5) evidence-based policy assessment. RESULTS: Of the nine provinces analysed, British Columbia ranked highest in Canada and Saskatchewan lowest. British Columbia had a morbidity and mortality rate that was close to the Canadian average and decreased over the study period. British Columbia also had a number of injury prevention policies and legislation in place that followed best practice guidelines. Saskatchewan had a higher rate of injury hospitalisation and death; however, Saskatchewan's rate decreased over time. Saskatchewan had a number of prevention policies in place but had not enacted bicycle helmet legislation. CONCLUSIONS: Future preventative efforts should focus on harmonising policies across all provinces in Canada that reflect evidence-based best practices.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".