A global survey exploring tackle training knowledge, attitudes and practices of women's rugby union coaches
Bibliographic record
Abstract
The coaching and performance context in women's rugby is not well understood, despite growing popularity worldwide. The aim of this study was to describe the knowledge, attitudes and tackle training practices of women's rugby coaches in relation to tackle safety and performance. A globally distributed online survey exploring coaches’ knowledge, attitudes and practices towards tackling women's rugby was completed by 357 coaches (mean age 41 ± 0.6 years) from 58 countries. The cross-sectional survey was distributed from March 2023 to June 2023. Survey development was guided by the Health Action Process Approach and informed by coaching experts, research evidence synthesis, and guidelines for international tackle safety programmes. Coaches believed that the risk of tackle injury in women's rugby is high and that proper tackle technique for safety is very important. More than 75% of coaches had never completed a tackle-specific training course, with only 39% aware of the availability of such courses. Time spent on controlled-contact and full-contact activities varied from 0 to 40+ min per week, averaging 10–20 min for both types of training. Barriers to the effectiveness of tackle training related to sociocultural factors, coach knowledge gaps, training environments, and player training considerations. Coach education, improved infrastructures, and physical development were ranked the highest priorities for improving tackle safety and performance. These findings inform future implementation strategies for tackle safety and performance in the context of women's rugby, highlighting the need to involve coaches in providing practical solutions, and the role of education and institutional/organisational support in facilitating such improvements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".