Can Referees Assess Head Contact Penalties Correctly in Canadian Youth Ice Hockey? A Video Analysis Study
Bibliographic record
Abstract
OBJECTIVE: To help address the high concussion burden in Canadian youth ice hockey, our primary objective was to examine the concurrent validity of youth ice hockey referees' ability to assess head contacts (HCs) and associated penalties using video analysis methods after implementation of the "zero tolerance for HC" policy by Hockey Canada. STUDY DESIGN: Cross-sectional study. PARTICIPANTS: Certified Level II-III referees in Alberta, Canada. INTERVENTION: A secured online survey with 60 videos (10 to 15 seconds) containing a player-to-player physical contact with or without a HC from elite U15 (ages 13 to 14) youth ice hockey games. OUTCOME MEASURES: Survey questions were completed by all referees for each video, including (1). 'Did you see a player-to-player contact?', (2). 'Should a penalty be assessed?', and if yes, (3). 'Which player, penalty type, and penalty intensity?' Referee assessments were compared with a consensus agreement from 2 national and member (top level) gold standard referees for concurrent validity through percent agreement and sensitivity/specificity measures. RESULTS: Complete-case analysis of 100 referees (131 recruited) showed an overall median agreement of 83.5% (sensitivity = 0.74; specificity = 0.69) with the gold standard. Agreement with the gold standard was highest for HC infractions [85.1% (sensitivity = 0.80; specificity = 0.69)], followed by HC penalty type (81.5%) and penalty intensity (53.7%). CONCLUSIONS: Concurrent validity through percent agreement was high (>80%) compared with the gold standard for identifying both HC and other infractions; however, it was moderate for penalty intensity. Although knowledge of identifying HCs and penalties in this survey was acceptable, this study suggests in-game factors (eg, game management and positioning) may be a primary limitation for HC enforcement.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".