Helmets for skiing and snowboarding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In Canada, winter sports injuries are responsible for significant health care burden, with estimates of $400 million in direct and indirect annual health care costs. For ski-related injuries, helmets have been shown to provide significant protection. Current common practice in Canada, including the Province of Nova Scotia, is to leave the decision of whether to wear a helmet to the individual. The purposes of this study were to document skiers' and snowboarders' use of helmets and to isolate factors associated with helmet use and nonuse. METHODS: A mixed methods approach was used to collect data during a 2-month period at the province's three ski hills. Naturalistic observations documented helmet use and falls, whereas interviews identified factors influencing helmet use or nonuse. RESULTS: Helmets were used by most skiers (74%) and snowboarders (72%); the use varied significantly between ski hills, ranging from 69% to 79%. Females were more likely to wear helmets compare with males (80% vs. 70%). The highest rates of use were found among 4-year-old to 12-year-old children, with helmet use declining as age increases. Qualitative data revealed that helmet users were most influenced by the protective benefits of helmets (77%), personal choice (46%), family (44%), and rules (44%), while non-helmet users cited personal choice (29%), comfort (26%), rules (14%), and cost (11%) as reasons for nonuse. CONCLUSION: More than 25% of skiers and snowboarders remain at increased risk of a serious brain injury by not wearing a helmet. Changes in regulations may be required to ensure widespread use of helmets on ski hills. LEVEL OF EVIDENCE: Prognostic study, level II.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it