Achieving all-age helmet use compliance for snow sports: strategic use of education, legislation and enforcement
Bibliographic record
Abstract
BACKGROUND: Nova Scotia is the first jurisdiction in the world to mandate ski and snowboard helmet use for all ages at ski hills in the province. This study represents a longitudinal examination of the effects of social marketing, educational campaigns and the introduction of helmet legislation on all-age snow sport helmet use in Nova Scotia. METHODS: A baseline observational study was conducted to establish the threshold of ski and snowboarding helmet use. Based on focus groups and interviews, a social marketing campaign was designed and implemented to address factors influencing helmet use. A prelegislation observational study assessed the effects of social marketing and educational promotion on helmet use. After all-age snow sport helmet legislation was enacted and enforced, a postlegislation observational study was conducted to determine helmet use prevalence. RESULTS: Baseline data revealed that 74% of skiers and snowboarders were using helmets, of which 80% were females and 70% were males. Helmet use was high in children (96%), but decreased with increasing age. Following educational and social marketing campaigns, overall helmet use increased to 90%. After helmet legislation was enacted, 100% compliance was observed at ski hills in Nova Scotia. CONCLUSIONS: Results from this study demonstrate that a multifaceted approach, including education, legislation and enforcement, was effective in achieving full helmet compliance among all ages of skiers and snowboarders.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".