HELMET USE AMONG SKIERS AND SNOWBOARDERS IN SOUTHERN ALBERTA
Bibliographic record
Abstract
Background There is little information on ski/snowboard helmet prevalence at ski areas in southern Alberta, with no published reports on correct helmet fit or helmet standards. Objective To determine the prevalence of helmet use and correct helmet fit among skiers/snowboarders at an Alberta ski area. Design Cross-sectional survey. Setting An urban ski area in Alberta, Canada. Participants Skiers/snowboarders of all ages. Risk factor assessment Skiers/snowboarders were interviewed (n=914) and systematically observed (n=2014) at the ski area. Main outcome measurements Helmet use and correct helmet fit. Prevalence ratios (PR) with 95% confidence intervals (CI) were calculated for each outcome separately for interviews and systematic observations. Results Preliminary results for helmet use indicate that the likelihood of wearing a helmet is higher for those <13 years old (PR 1.09; 95% CI 1.01–1.17) and for 13–17 year olds (PR 1.13; 95% CI 1.07–1.20) compared with older participants; skiing/snowboarding with adults (PR 1.24; 95% CI 1.11–1.40) vs. alone, and if adult companions were wearing a helmet (PR 3.01; 95% CI 1.71–5.30). Preliminary results for helmet fit indicate that the likelihood of wearing a helmet correctly increases if skiing/snowboarding with adults and children (PR 2.86; 95% CI 1.28–6.36) vs. no companions, and if the child was wearing a helmet (PR 4.45; 95%CI 1.22− 16.28). The likelihood of wearing a helmet correctly was lower for ages 13–17 (PR 0.80; 95% CI 0.69–0.93) compared with 18–54 and for snowboarders (PR 0.87; 95% CI 0.77–0.97) compared with skiers. Conclusions This information has the potential to influence policy regarding mandatory helmet use and may be used to educate those with an interest in helmet safety and the public on the use and correct use of a helmet.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".