High Injury and Concussion Rates in Female Youth Team Sport: An Opportunity for Prevention
Bibliographic record
Abstract
The aim of this study was to investigate sport-related injury rates, types, locations, and mechanisms in female youth team sports. This was a secondary analysis of a cross-sectional study. An anonymous online survey was administered to high school students (ages 14-19) in Alberta, Canada. The survey included questions regarding demographic information, sport participation and self-reported injuries sustained in the past year. Results were analyzed for girls who reported playing a top ten team sports for female participation. For girls participating in team sports, the overall injury rate was 55.5 injuries/100 participants/year. The rate of at least one concussion was 9.4 concussions/100 participants/year. Injury and concussion rates were highest in ringette (Injury rate=42.9 injuries/100 participants/year, Concussion rate=19.0 concussions/100 participants/year) and rugby (Injury rate=40.0, Concussion rate=15.3). The top three most serious injury locations were the knee (24.7%), ankle (21.6%) and head (16.1%). The most common injury types were joint/ligament sprain (26.71%), fracture (13.0%) and concussion (11.8%). Contact mechanisms accounted for 73.4% of all serious injuries reported in girls team sports. Team sport injury rates are high in female youth team sports. Specific consideration of sport-specific injury rates, types and mechanisms in girls' team sports will inform development and evaluation of targeted sport-specific prevention strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".