Barriers and Enablers to Enacting Child and Youth Related Injury Prevention Legislation in Canada
Bibliographic record
Abstract
Injury prevention policy is crucial for the safety of Canada’s children; however legislation is not adopted uniformly across the country. This study aimed to identify key barriers and enablers to enacting injury prevention legislation. Purposive snowball sampling identified individuals involved in injury prevention throughout Canada. An online survey asked respondents to identify policies that were relevant to them, and whether legislation existed in their province. Respondents rated the importance of barriers or enablers using a 5-point Likert type scale and included open-ended comments. Fifty-seven respondents identified the most common injury topics: bicycle helmets (44, 77%), cell phone-distracted driving (36, 63%), booster seats (28, 49%), ski helmets (24, 42%), and graduated driver’s licensing (21, 37%). The top enablers were research/surveillance, managerial/political support and professional group consultation, with much variability between injury topics. Open-ended comments emphasized the importance of a united opinion as an enabler and barriers included costs of protective equipment and inadequate enforcement of legislation. The results highlighted the importance of strategies that include research, management and community collaboration and that injury prevention topics should be addressed individually as information may be lost if topics are considered together. Findings can inform the process of turning injury prevention evidence into action.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".