Where's your head at? Perceptions and utilisation of concussion prevention and rehabilitation strategies in women's and men's rugby union
Bibliographic record
Abstract
OBJECTIVES: Rugby union (rugby) is a popular collision sport with high concussion rates. As such, concussion prevention and rehabilitation strategies are critical to player welfare in the sport. This study aims to identify and analyse the concussion prevention and rehabilitation strategies currently implemented across different rugby contexts. DESIGN: A cross-sectional study targeting medical professionals and coaches globally through an online survey distributed via email and social media. METHODS: A validated, piloted survey was developed in collaboration with subject-matter experts and delivered using REDCap, with participants (aged 18+) from medical and coaching backgrounds. RESULTS: A total of 172 respondents (77 medical professionals, 89 coaches, 6 "other") responded. Training strategies and policy changes were perceived as the most important primary prevention strategies, whilst all secondary (education, recognise and remove) and tertiary (return to play processes and rehabilitation) strategies were deemed highly important. Despite this, utilisation varied widely. Perceived importance and utilisation of rehabilitation strategies also varied. The main barriers to implementation were a lack of understanding (47%) and time constraints (45%). There were no significant differences in odds of return-to-play use by level of play or sex; however, there was a significantly lower odds of medical return-to-play sign-off with lower levels of play (odds ratio: 0.77, 95% confidence intervals: 0.61-0.98). CONCLUSIONS: This study underscores the variation in perceived importance and utilisation of prevention and rehabilitation strategies in rugby. It emphasises the necessity for education to help remove barriers with the aim of optimising programme implementation and utility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".