Gender- and/or sex-specific considerations for sport-related injury: a concept mapping approach for the Female, woman and/or girl Athlete Injury pRevention (FAIR) consensus
Bibliographic record
Abstract
Objective This study aimed to gather and represent experts’ perspectives on the gender- and/or sex-specific factors relevant to injury risk for female/woman/girl athletes. Methods Mixed-methods concept mapping study. Sixty-six experts including cisgendered (1) athlete/coach/carers, (2) clinicians, (3) sports science/high-performance professional, (4) administrators and (5) researchers brainstormed statements to a prompt (‘What gender-specific and/or sex-specific factors do you think contribute to injury risk among female, woman and girl athletes?’) before thematically sorting and rating the statements/factors for importance and modifiability (5-point Likert scales). Results Ten clusters were constructed from 101 unique statements/factors. The clusters (number of statements) include: (1) Inequitable organisational funding and support (n=17); (2) Athletes’ lack of, and access to, resources (n=7); (3) Lack of knowledge and expertise among support staff (n=6); (4) Lack of evidence for, and implementation of gender and sex-appropriate injury prevention (n=20); (5) Sex-related factor s (n=14); (6) Gendered health (n=8); (7) Gendered expectations to conform to athletic ideals and norms (n=10); (8) Gendered harassment (interpersonal violence) and social biases (n=9); (9) Gendered sport environment (7); (10) Gendered communication (n=3). Lack of knowledge and expertise among support staff was deemed the most important and modifiable cluster to address gender- and/or sex-specific factors relevant to injury prevention for female/woman/girl athletes. Conclusion Ten gender- and/or sex-specific clusters, ranging from organisational to biological considerations and societal influences, were defined that could impact female/woman/girl athlete injury risk factors. Advancing stronger evidence for gender and sex appropriate injury prevention is urgently needed.
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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 | 0.074 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.010 |
| 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".