Prevalence and diffusion of helmet use at ski areas in Western North America in 2001–02
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine diffusion of and predictors of helmet use among skiers and snowboarders in the Western United States and Canada. DESIGN: 6400 skiers and snowboarders at 29 ski resorts in the Western United States and Canada were interviewed on chair lifts and observed for helmet use during two consecutive ski seasons (winters 2001 and 2002). SETTING: Skiers and snowboarders were observed and interviewed at 29 ski resorts in Alaska, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, and British Columbia as part of a sun protection project. SUBJECTS: Participants completing the survey consisted of 3525 adult skiers and snowboarders in the 2002 season and 2978 adult skiers and snowboarders in the 2001 season. MAIN OUTCOME MEASURE: The outcome measure for all analyses was prevalence of helmet use by skiers and snowboarders. RESULTS: Helmet use by skiers and snowboarders is increasing and is most prevalent among snowboarders, experts, and more frequent skiers/snowboarders. No evidence was found for the hypothesis that helmet use is diffusing more rapidly among earlier adopters of helmets than later adopters. CONCLUSIONS: Although controversy remains, helmets are rapidly diffusing as a safety device at western North American ski resorts. Expert and more frequent skiers and snowboarders are more likely to wear helmets, which may indicate that helmets are recognized as a safety device.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".