Aggression, Violence and Injury in Minor League Ice Hockey: Avenues for Prevention of Injury
Bibliographic record
Abstract
BACKGROUND: In North America, more than 800,000 youth are registered in organized ice hockey leagues. Despite the many benefits of involvement, young players are at significant risk for injury. Body-checking and aggressive play are associated with high frequency of game-related injury including concussion. We conducted a qualitative study to understand why youth ice hockey players engage in aggressive, injury-prone behaviours on the ice. METHODS: Semi-structured interviews were conducted with 61 minor ice hockey participants, including male and female players, parents, coaches, trainers, managers and a game official. Players were aged 13-15 playing on competitive body checking teams or on non-body checking teams. Interviews were manually transcribed, coded and analyzed for themes relating to aggressive play in minor ice hockey. RESULTS: Parents, coaches, teammates and the media exert a large influence on player behavior. Aggressive behavior is often reinforced by the player's social environment and justified by players to demonstrate loyalty to teammates and especially injured teammates by seeking revenge particularly in competitive, body-checking leagues. Among female and male players in non-body checking organizations, aggressive play is not reinforced by the social environment. These findings are discussed within the framework of social identity theory and social learning theory, in order to understand players' need to seek revenge and how the social environment reinforces aggressive behaviors. CONCLUSION: This study provides a better understanding of the players' motivations and environmental influences around aggressive and violent play which may be conducive to injury. The findings can be used to help design interventions aimed at reducing aggression and related injuries sustained during ice hockey and sports with similar cultures and rules.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".