Effectiveness of an educational video on concussion knowledge in minor league hockey players: a cluster randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: With the heightened awareness of concussions in all sports, the development and implementation of effective prevention strategies are necessary. Education has been advocated as an effective injury prevention intervention. PURPOSE: To examine the effectiveness of the 'Smart Hockey: More Safety, More Fun' video on knowledge transfer among minor league hockey players. STUDY DESIGN: Cluster-randomised controlled trial. METHODS: A total of 267 participants from two age divisions and competitive levels were assigned to either a video or no-video group. The video was shown (or not shown) to the entire team as a result of random assignment. To evaluate the effectiveness of the educational video, questionnaires specific to concussion knowledge and players' attitudes and behaviours were completed. RESULTS: There was a significant increase in the players' concussion knowledge scores immediately following exposure to the video (F(1,103)=27.00, p<0.001). However, concussion knowledge at 2 months was not significantly different between the video and no-video groups, after controlling for prior knowledge level, age and competitive level (F(1,115)=0.41, p=0.523). Similarly, players' attitudes and behaviour scores at 2 months did not differ between groups (F(1,115)=0.41, p=0.507). CONCLUSIONS: We were able to show that a single viewing of an educational video in hockey could immediately improve knowledge about concussion but that this effect was transient and lost at 2-month follow-up. Future prevention endeavours in hockey and other sports should attempt to incorporate strategies and modalities to enhance knowledge retention.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".