Evaluation of the ThinkFirst Canada, <i>Smart Hockey</i>, brain and spinal cord injury prevention video
Bibliographic record
Abstract
OBJECTIVE: The ThinkFirst Canada Smart Hockey program is an educational injury prevention video that teaches the mechanisms, consequences, and prevention of brain and spinal cord injury in ice hockey. This study evaluates knowledge transfer and behavioural outcomes in 11-12 year old hockey players who viewed the video. DESIGN: Randomized controlled design. SETTING: Greater Toronto Minor Hockey League, Toronto Ontario. SUBJECTS: Minor, competitive 11-12 year old male ice hockey players and hockey team coaches. INTERVENTIONS: The Smart Hockey video was shown to experimental teams at mid-season. An interview was conducted with coaches to understand reasons to accept or refuse the injury prevention video. MAIN OUTCOME MEASURES: A test of concussion knowledge was administered before, immediately after, and three months after exposure to the video. The incidence of aggressive penalties was measured before and after viewing the video. RESULTS: The number of causes and mechanisms of concussion named by players increased from 1.13 to 2.47 and from 0.67 to 1.22 respectively. This effect was maintained at three months. There was no significant change in control teams. There was no significant change in total penalties after video exposure; however, specific body checking related penalties were significantly reduced in the experimental group. CONCLUSION: This study showed some improvements in knowledge and behaviours after a single viewing of a video; however, these findings require confirmation with a larger sample to understand the sociobehavioural aspects of sport that determine the effectiveness and acceptance of injury prevention interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".