Testing the risk compensation hypothesis for safety helmets in alpine skiing and snowboarding
Bibliographic record
Abstract
OBJECTIVE: The prevalence of helmet use by alpine skiers and snowboarders was estimated and self-reports on risk taking were assessed to test for potential risk compensation when using helmets in these sports. SETTING: Skiers and snowboarders were observed and interviewed at 34 resorts in the western United States and Canada. SUBJECTS: Respondents were 1779 adult skiers and snowboarders in the 2003 ski season. OUTCOME MEASURES: Observations of helmet use and questions about perceived speed and degree of challenge when not wearing a helmet (helmet wearers) or in previous ski seasons (non-helmet wearers). RESULTS: Helmet wearers reported that they skied/snowboarded at slower speeds (OR = 0.64, p<0.05) and challenged themselves less (OR = 0.76, p<0.05) than non-helmet wearers. Adoption of safety helmets in 2003 (23%) continued to increase over 2002 (OR = 0.46, p<0.05) and 2001 (OR = 0.84, p<0.05). CONCLUSIONS: No evidence of risk compensation among helmet wearers was found. Decisions to wear helmets may be part of a risk reduction orientation. Helmet use continues to trend upwards but adoption may be slowing.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".