Helmet Use Among Skiers and Snowboarders in Southern Alberta
Bibliographic record
Abstract
Skiing and snowboarding are among the most popular winter activities in Canada. Unfortunately, many injuries occur in these activities, including severe injuries often involving the head, which can result in death. These injuries, particularly severe head or brain injuries, are an important burden on the health care system, and may be prevented by encouraging helmet use. Helmet effectiveness is well established, but there are little recent data on ski and snowboard helmet prevalence at ski areas in southern Alberta, with no published reports on correct helmet fit. This study determined the prevalence of helmet use and correct helmet use at a single ski area in Southern Alberta and examined the factors associated with helmet use and correct fit. Information on helmet use and correct use, as well as environmental and behavioural characteristics was collected at the base of the ski hill by both interviewing and observing participants. Age (<18 years old), skiing/snowboarding with companions, and skiing/snowboarding with companions also wearing helmets increased the likelihood of each wearing a helmet and wearing a helmet correctly. The protective effect of helmets in skiers and snowboarders has been demonstrated convincingly. The education and promotion of helmet use and correct helmet use is paramount in reducing the risk of head injuries among skiers and snowboarders. Our findings will inform participants, parents, members of the ski-snowboard industry, and the stakeholders that seek to influence them to improve the safety of skiers and snowboarders of all ages.
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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 | 0.002 | 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) | 0.001 | 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".