Is ski helmet legislation more effective than education?
Bibliographic record
Abstract
Annually, several hundred million people worldwide enjoy alpine skiing and snowboarding.1 Besides the well-known beneficial effects related to exercise, these snow sports are also associated with a certain risk of injury. Head injuries account for 9–19% of all winter sport injuries reported by ski patrols and emergency departments.1 ,2 However, the use of ski helmets has been shown to reduce the head injury risk up to 60% among children and adults.1 ,2 While in recent years ski helmet use has become mandatory for children in Italy and in most Austrian provinces,3 ,4 the worldwide first mandatory ski helmets for all ages was introduced in Nova Scotia (East Canada) in 2011.5 Although over the last 10 years ski helmet use has steadily increased worldwide, for example, up to 70% in Canada, Austria and Switzerland in 2010,4 ,5 there is an ongoing debate in various countries about the introduction of mandatory ski helmets.4 ,6 Therefore, question arises as to whether ski helmet legislation is more effective regarding an increasing helmet use than education. To our knowledge, only one study has investigated the impact of mandatory ski helmets on …
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".