11.37 Physical contact and suspected injury metrics in male vs female youth ice hockey: a video-analysis study
Bibliographic record
Abstract
Objective Canada’s national winter sport of ice hockey has high youth participation, however, research surrounding the female game is limited and the injury burden remains high. This study aimed to compare the incidence of head contact (HC), high-intensity player-to-player contact known as body checking (BC; prohibited in the female game), and suspected concussion between male and female youth ice hockey. Design Cross-sectional. Setting Game video-recordings captured in Calgary, Canada. Participants Ten male and ten female elite U15AA (13–14-year-old) game video-recordings collected in the 2020–21 and 2021–22 seasons, respectively. Assessment of Risk Factors An analysis of physical contact and injury mechanisms using video-analysis. Outcome Measures Videos were analyzed frame-by-frame in Dartfish video-analysis software and all player contacts were tagged including HCs [direct (HC1), indirect (HC2)], BCs (level 4–5 trunk contact on a 1–5 scale), and suspected concussion based on validated criteria. Univariate Poisson regression clustering by team-game offset by game-length was used to estimate incidence rates (IR) and incidence rate ratios (IRR, 95% confidence intervals). Main Results There were no significant differences in the rates of direct HC (IRMale=8.88/100team-minutes; IRFemale=9.30/100team-minutes; IRR=1.04, 0.77–1.42) or suspected concussion (IRMale=0.59/100team-minutes; IRFemale=0.25/100team-minutes; IRR=0.42, 0.12–1.42) between cohorts. A 13% lower rate of total physical contacts was found in the female game (IRR=0.87, 0.79–0.96) with 70% lower rates of BC (IRR=0.30, 0.23–0.39). Although prohibited in the female game, only 5.4% of HC1s and 18.6% of BC resulted in a penalty. Conclusions Rates of direct HCs and suspected concussion were similar in male and female youth ice hockey. BC rates were lower in the female game, yet still prevalent despite being prohibited.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".