‘Body on the line’: experiences of tackle injury in women’s rugby union – a grounded theory study
Bibliographic record
Abstract
OBJECTIVES: Tackle-related injuries account for up to 67% of all match injuries in women's rugby union. The perspective of women players on tackle injury can help key stakeholders understand psychosocial determinants of tackle injury risk and prevention. We aimed to capture psychosocial processes that explain tackle injury experiences and behaviours in women's rugby union. METHODS: We conducted a qualitative study using a grounded theory approach. Adult women players, with at least 1-year senior level experience, were recruited from Europe, South Africa and Canada between December 2021 and March 2022. Data were collected through semistructured interviews and analysed in line with grounded theory coding procedures. RESULTS: Twenty-one players, aged 20-48 years with a mean 10.6 years of rugby playing experience, participated. In our analysis, we identified three categories central to participants' experiences of tackle injury: (1) embodied understandings of tackle injury, (2) gender and tackle injury risk and (3) influences on tackle injury behaviours. Participants reported a sense of fear in their experience of tackling but felt that tackle injuries were an inevitable part of the game. Tackle injury was described based on performance limitations. Tackle injury risks and behaviours were influenced by gendered factors perpetuated by relations, practices and structures within the playing context of women's rugby union. CONCLUSION: Women's tackle injury experiences were intertwined with the day-to-day realities of marginalisation and under preparedness. Grounded in the voices of women, we have provided recommendations for key stakeholders to support tackle injury prevention in women's rugby.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".