Effectiveness of interventions to reduce aggression and injuries among ice hockey players: a systematic review
Bibliographic record
Abstract
BACKGROUND: The increasing incidence of injuries related to playing ice hockey is an important public health issue. We conducted a systematic review to evaluate the effectiveness of interventions designed to reduce injuries related to aggressive acts in ice hockey. METHODS: We identified relevant articles by searching electronic databases from their inception through July 2012, by using Internet search engines, and by manually searching sports medicine journals, the book series Safety in Ice Hockey and reference lists of included articles. We included studies that evaluated interventions to reduce aggression-related injuries and reported ratings of aggressive behaviour or rates of penalties or injuries. RESULTS: We identified 18 eligible studies. Most involved players in minor hockey leagues. Of 13 studies that evaluated changes in mandatory rules intended to lessen aggression (most commonly the restriction of body-checking), 11 observed a reduction in penalty or injury rates associated with rule changes, and 9 of these showed a statistically significant decrease. The mean number of penalties decreased by 1.2-5.9 per game, and injury rates decreased 3- to 12-fold. All 3 studies of educational interventions showed a reduction in penalty rates, but they were not powered or designed to show a change in injury rates. In 2 studies of cognitive behavioural interventions, reductions in aggressive behaviours were observed. INTERPRETATION: Changes to mandatory rules were associated with reductions in penalties for aggressive acts and in injuries related to aggression among ice hockey players. Effects of educational and cognitive behavioural interventions on injury rates are less clear. Well-designed studies of multifaceted strategies that combine such approaches are required.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".