Helmets for skiing and snowboarding
Bibliographic record
Abstract
BACKGROUND: In Canada, winter sports injuries are responsible for significant health care burden, with estimates of $400 million in direct and indirect annual health care costs. For ski-related injuries, helmets have been shown to provide significant protection. Current common practice in Canada, including the Province of Nova Scotia, is to leave the decision of whether to wear a helmet to the individual. The purposes of this study were to document skiers' and snowboarders' use of helmets and to isolate factors associated with helmet use and nonuse. METHODS: A mixed methods approach was used to collect data during a 2-month period at the province's three ski hills. Naturalistic observations documented helmet use and falls, whereas interviews identified factors influencing helmet use or nonuse. RESULTS: Helmets were used by most skiers (74%) and snowboarders (72%); the use varied significantly between ski hills, ranging from 69% to 79%. Females were more likely to wear helmets compare with males (80% vs. 70%). The highest rates of use were found among 4-year-old to 12-year-old children, with helmet use declining as age increases. Qualitative data revealed that helmet users were most influenced by the protective benefits of helmets (77%), personal choice (46%), family (44%), and rules (44%), while non-helmet users cited personal choice (29%), comfort (26%), rules (14%), and cost (11%) as reasons for nonuse. CONCLUSION: More than 25% of skiers and snowboarders remain at increased risk of a serious brain injury by not wearing a helmet. Changes in regulations may be required to ensure widespread use of helmets on ski hills. LEVEL OF EVIDENCE: Prognostic study, level II.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".