133 Canadian child safety report card: a comparison of injury prevention practices across provinces
Bibliographic record
Abstract
Background Health-based report cards have been used as a tool to disseminate research findings to parents, government agencies, stakeholders, and the general public. In Canada, health-based report cards such as the How Canada Performs report provides a comparison of how provinces measure up to one another on a number of health-based indicators. However, few child health report cards discuss implications for primary prevention policy or practice. Methods This project plans to develop and communicate child safety report cards for each of the 10 Canadian provinces in 3 phases. Phase I is an interprovincial comparison of injury hospitalizations in each Canadian province over time. Phase II is an examination of evidence-based provincial policies. Phase III combines results from I and II and creates Canadian Child Safety Report Cards for each province. Results In Canada, Saskatchewan was the province with the highest rate of injury hospitalisation per 100,000 between 2006 and 2012, but incidence decreased from 967 to 852 over the 6 year period, despite not having policies that meet best practice. Ontario had the lowest rate of injury hospitalisation per 100,000, however the incidence rate increased slightly from 451 to 479. Only British Columbia decreased the incidence of injuries compared to the Canadian average. The rate decreased from 667 to 515 between 2006 and 2012. This change in incidence over time is observed in a province that complied with best practice evidence-based injury prevention policies. Conclusions This is the first study to compare injuries among children and youth across Canadian provinces in terms of hospitalisation, and the enactment of evidence-based policies. This data may allow the influence of all spectrums of prevention by resulting in the harmonisation of policy and legislation in Canada. Similar projects in the European Union have started to yield results in terms of harmonising prevention policies across member states.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".