Hockey referees: are they part of the solution for concussions in youth hockey?
Bibliographic record
Abstract
Rationale/purpose Concussions pose short – and long-term risks for youth athletes. To mitigate these risks, sport organizations must find ways to enhance athlete safety, including prompt removal of athletes who display signs and symptoms of concussions. Some sport organizations have incorporated officials in concussion prevention and management strategies. The purpose of this study was to gain sport officials’ perspectives of being able to remove athletes with suspected concussion from games.Design/methodology/approach We conducted a qualitative study using semi-structured interviews with 10 ice hockey officials from across Canada. We implemented a pragmatic approach with a reflexive thematic analysis to maintain the participants’ truths and perspectives within the results.Findings Our analysis resulted in the four themes: (a) Considerations and Importance of the Protocol, (b) Officials’ Knowledge of Concussions, (c) Required Training, and (d) Barriers.Practical Implications Removing athletes with suspected concussions could be added to officials’ duties; however, hesitancy exists regarding implementation and effectiveness of the protocol. Focus on the implementation and education of the officials is required.Research Contribution This study contributes to the growing literature on sport officials and their role with respect to concussions in youth ice hockey.
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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 | 0.031 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".