Higher Rates of Head Contacts, Body Checking, and Suspected Injuries in Ringette Than Female Ice Hockey: Time to Ring in Opportunities for Prevention
Bibliographic record
Abstract
OBJECTIVE: Ringette and female ice hockey are high participation sports in Canada. Despite policies disallowing body checking, both sports have high injury and concussion rates. This study aimed to compare physical contact (PC), head contact (HC), and suspected injury and concussion incidence rates (IRs) in female varsity ringette and ice hockey. DESIGN: Cross-sectional. SETTING: Canadian ice arenas. PARTICIPANTS: Eighteen Canadian female university ringette and ice hockey tournament/playoff games in the 2018-2019/2019-2020 seasons. ASSESSMENT OF RISK FACTORS: Game video-recordings were analyzed using Dartfish video-analysis software to compare both sports. MAIN OUTCOME MEASURES: Univariate Poisson regression analyses (adjusted for cluster by team, offset by game-minutes) were used to estimate PC, HC, and suspected injury IRs and incidence rate ratios (IRRs) to compare rates across sports. Proportions of body checks (level 4-5 trunk PC) and direct HC (HC 1 ) penalized were reported. RESULTS: Analyses of 36 team-games (n = 18 ringette, n = 18 hockey) revealed a 19% lower rate of PCs in ringette than ice hockey {IRR = 0.81 [95% confidence interval (CI), 0.73-0.90]}, but a 98% higher rate of body checking [IRR = 1.98 (95% CI, 1.27-3.09)] compared to ice hockey. Ringette had a 40% higher rate of all HC 1 s [IRR = 1.40 (95% CI, 1.00-1.96)] and a 3-fold higher rate of suspected injury [IRR = 3.11 (95% CI, 1.13-8.60)] than ice hockey. The proportion of penalized body checks and HC 1 s were low across sports. CONCLUSIONS: Body checking and HC 1 rates were significantly higher in ringette compared to ice hockey, despite rules disallowing both, and very few were penalized. These findings will inform future injury prevention research in ringette and female ice hockey.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".