Physical Contact and Suspected Injury Rates in Female versus Male 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 female ice hockey is limited and the injury burden remains high. This study compared rates of head contact (HC), body checking (BC; high-intensity player-to-player contact), and suspected concussion between female and male youth ice hockey. DESIGN: Cross-sectional. SETTING: Game video-recordings captured in Calgary, Canada. PARTICIPANTS: Ten female (BC prohibited) and 10 male (BC permitted) U15 elite AA (13-14-year-old) game video-recordings collected in the 2021 to 22 seasons and 2020 to 21, respectively. ASSESSMENT OF RISK FACTORS: An analysis of player-to-player physical contact and injury mechanisms using video-analysis. MAIN OUTCOME MEASURES: Videos were analyzed in Dartfish video-analysis software and all physical contacts were coded based on validated criteria, including HCs (direct [HC1], indirect [HC2]), BC (levels 4-5 on a 5-point intensity scale), and video-identified suspected concussions. Univariate Poisson regression clustering by team-game offset by game-length (minutes) were used to estimate incidence rates and incidence rate ratios (IRR, 95% confidence intervals). RESULTS: The female game had a 13% lower rate of total physical contacts (IRR = 0.87, 0.79-0.96) and 70% lower rate of BC (IRR = 0.30, 0.23-0.39). There were however no differences in the rates of direct HC (IRR = 1.04, 0.77-1.42) or suspected concussion (IRR = 0.42, 0.12-1.42) between the cohorts. Although prohibited in the female game, only 5.4% of HC1s and 18.6% of BC resulted in a penalty. CONCLUSIONS: The rates of HC1s and suspected concussions were similar across 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".