A Collaborative Approach to Co‐Creating Contact Confident, an Evidence‐Informed Tackle Safety and Technique Intervention for Coaches and Players in Rugby Union
Bibliographic record
Abstract
Training strategies to promote safe and effective tackle technique are an important target for injury prevention and enhancing performance across the rugby codes. However, there is a research-to-implementation gap in 'real-world' settings and a need for more studies with women and girls. This article outlines the development of an evidence-informed tackle coaching intervention co-created with content and context experts in women's rugby union. Based on previous work which developed a context-specific injury-prevention programme for women playing Australian Football, a 7-step process was adopted. After gaining organisational support, the process included using research evidence and applied experience and engaging intervention implementers to co-create the content for 'Contact Confident'. Iterative integration of feedback from early implementers enhanced practical relevance for coaches. This study underlines the importance of stakeholder collaboration in cocreating and implementing injury prevention interventions, offering a scalable resource for tackle and ball carrier skill development in rugby union for women and girls, with wider relevance for other genders and sports. Future research should look to evaluate the impact of this and similar context-specific interventions on coach behaviour and athlete outcomes across varied global rugby settings.
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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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".