Gendered environmental pathways to sports injury: insights from retired athletes in the UK high-performance context
Bibliographic record
Abstract
OBJECTIVE: Women remain at increased risk for some sports injuries, such as anterior cruciate ligament rupture and concussion. This study applied a gendered environmental approach to identify modifiable features of women's sport environments that may contribute to the gendered patterning of sports injuries. Our objectives were to identify features of gendered environments that mattered in athletes' lived experiences and to trace pathways connecting these features to injury. METHODS: We employed a creative methodology combining semi-structured interviews with artefact-elicited storytelling and poetic transcription to actively centre women athletes' voices and communicate their experiences in formats intended to stimulate reflection among sport system stakeholders. RESULTS: Drawing on insights from 20 recently retired women athletes across 11 UK high-performance sports, our reflexive thematic analysis identified five gendered environmental challenges shaping women's injury experiences, risk and outcomes: (1) stereotypes trivialise injury, (2) physiology is all or nothing, (3) the 'ideal' female athlete, (4) in/visible inequities and (5) uneven power dynamics. Within these gendered environmental challenges, we identified mechanisms through which challenges manifest in the everyday experiences of athletes, highlighting these as potential points to disrupt the gendered environments-to-injury pathway. CONCLUSION: Our findings provide an evidence-based framework for categorising and addressing gendered environmental challenges in women's sport. Interventions to reconfigure the gendered status quo within sport should be embedded as part of injury prevention strategies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".