Differences in injury and concussion rates in a cohort of Canadian female and male youth Rugby Union: a step towards targeted prevention strategies
Bibliographic record
Abstract
Objective To examine differences in match and training musculoskeletal injury and concussion rates and describe mechanisms of concussion while considering previous playing experience in female and male Canadian high school Rugby Union (‘rugby’) players. Methods A 2-year prospective cohort study was completed in a high school league (n=361 females, 421 player-seasons; n=429 males, 481 player-seasons) in Calgary, Canada over the 2018 and 2019 rugby playing seasons. Baseline testing was completed at the start of each season and injury surveillance and individual player participation through session attendance was documented to quantify individual-level player exposure hours. Injury incidence rates (IRs) and incidence rate ratios (IRRs) were calculated using Poisson regression, offset by player exposure hours and clustered by team. Results Overall match IR for females was 62% higher than males (overall IRR=1.62, 95% CI: 1.20 to 2.18) and the overall training IR was twice as high for females (overall IRR=2.15, 95% CI: 1.40 to 3.32). The female match concussion IR was 70% higher than the males (concussion IRR=1.70, 95% CI: 1.08 to 2.69). Females had a 75% greater tackle-related IR compared with males (IRR=1.75, 95% CI: 1.20 to 2.56). Additionally, female tacklers had a twofold greater rate of injury compared with male tacklers (IRR = 2.17, 95% CI: 1.14 to 4.14). Previous playing experience was not associated with tackle-related injury or concussion IRs. Conclusion The rate of injury and concussion was significantly higher in females within this Canadian high school cohort. These results emphasise the need for development, implementation and evaluation of female-specific injury and concussion prevention strategies to reduce injury and concussion in female youth rugby.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".