Examining Attitudes Toward Body Checking, Levels of Emotional Empathy, and Levels of Aggression in Body Checking and Non-Body Checking Youth Hockey Leagues
Bibliographic record
Abstract
OBJECTIVES: To determine if attitudes associated with body checking, emotional empathy, and aggression differ between players in body checking and non-body checking hockey leagues and to determine the influence of these attitudes on injury rates. DESIGN: Prospective cohort. SETTING: Participants were randomly recruited by team from the Calgary Minor Hockey Association at the beginning of the 2006-2007 season. PARTICIPANTS: There were 283 participants from Pee Wee (aged 11-12 years), Bantam (aged 13-14 years), and Midget (aged 15-16 years) teams. Of 13 teams from the body checking league, 138 players participated, and of 24 teams in the non-body checking league, 145 players participated. ASSESSMENT OF RISK FACTORS: Participants completed 4 self-report questionnaires: (1) Medical Questionnaire, (2) Body Checking Questionnaire, (3) Empathy Index for Children and Adolescents, and (4) Buss-Perry Aggression Questionnaire. MAIN OUTCOME MEASURES: Participants were followed through the season for injury reports. The injury definition included any hockey injury resulting in medical attention, the inability to complete a hockey session, and/or missing a subsequent hockey session. RESULTS: Body checking players reported more positive attitudes toward body checking (35.59; 95% confidence interval [CI]: 34.52-36.65) than non-body checking players (22.43; 95% CI, 21.38-23.49; t = -17.34; P < 0.00005). There was no significant difference in the empathy scores between cohorts (t = 1.51, P = 0.13). The mean aggression score for the body checking players (76.22; 95% CI, 73.18-79.25) was significantly higher than the mean for the non-body checking players (70.57; 95% CI, 67.35-73.80; t = -2.52; P = 0.013). CONCLUSIONS: Body checking seems to influence attitudes toward body checking and aggression, but attitudes toward body checking, empathy, and aggression did not influence injury rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".