Injury reporting and the use of injury prevention programmes in women's compared with men's rugby union players: A scoping review
Bibliographic record
Abstract
OBJECTIVES: Identify current injury surveillance and prevention literature in women's compared to men's rugby union players. DESIGN: Scoping review. METHODS: A two-step search strategy identified relevant published and unpublished literature on adult rugby players from five electronic databases, three governing body season report storage locations, and citation searches. Literature was screened against inclusion criteria for time-loss injury and/or injury prevention programmes and outcomes. Data was extracted and findings were reported using 1. a numerical analysis and 2. a thematic analysis. RESULTS: In total, 3196 articles were screened at abstract and full-text level, 252 met inclusion criteria. Across 252 studies were 330 cohorts, women-only cohorts accounted for 24 % (n = 79) of injury surveillance literature. Match injury incidence ranges were greater than training across all cohorts, men's and women's injury rates and severity across match and training were similar. Only 30 % of cohorts assessed training injury, 27 % in men's and 42 % in women's cohorts. General agreement highlighted lower limb, joint/ligament and concussion injuries to be most common across the men's and women's game. Risk factors were commonly assessed in men's literature (49 %) but reported less within women's research (25 %). Thirteen injury prevention studies were identified, two involved evaluation of injury prevention initiatives in women's cohorts. CONCLUSIONS: There is limited women's representation in rugby injury surveillance research compared to men's, and there is scarce evidence of the implementation and evaluation of injury prevention initiatives to reduce injury rates in women. Future research should focus on women's surveillance to inform injury prevention studies, implemented and evaluated in women's rugby cohorts.
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".