Assessing Helmet Fit for Varsity Ice Hockey Athletes: A Field Intervention Study
Bibliographic record
Abstract
Background: Protective equipment is at the forefront of the discussion on reducing sports-related concussions, with the role of helmets being equivocal. Data suggest that modern helmets are not entirely protective against concussions, and ill-fitting helmets may contribute to concussions with longer-lasting symptoms. Helmet fit can be assessed via a checklist, but its application outside of youth sport is lacking. Objectives: The purpose of this study was to assess helmet fit and the effect of feedback for a men’s (n=22) and a women’s (n=20) university varsity ice hockey team across a season. Methods: This field intervention study used a 12-item helmet fit checklist which was divided into five categories, defining “proper fit” as meeting all criteria of the stability and size categories. Players’ concussion histories and helmet fit knowledge were assessed using a survey. Results: The number of properly fitting helmets for males increased from 23% to 77% over the season, while remaining consistent at 50% for females. For males, 73% had an increase in overall checklist scores and 27% saw no change; 45% of females had an increase, 5% saw no change, and 50% decreased. Reported female (85%) concussion rate was double that of males (41%), but perceived knowledge of helmet fit (male=36%, female=30%) and actual knowledge (male=77%, female=85%) were similar. Conclusions: Feedback only had a positive effect on helmet fit for males across the season, potentially due to helmet style differences for females. Improvements to checklist criteria and scoring would increase the effectiveness of assessing fit for multiple helmet styles.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".