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Record W4414265739 · doi:10.1136/bjsports-2025-109946

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

2025· article· en· W4414265739 on OpenAlexafffund
Kay M. Crossley, M. Haberfield, Andrew G Ross, L. Gracias, Andrea M Bruder, Jackie L. Whittaker, Araba Chintoh, Jane S Thornton, Margie H. Davenport, Margo Mountjoy, Melanie Hayman, Brooke Patterson, Cheri Blauwet, Evert Verhagen, Carla van den Berg, Carole Akinyi Okoth, Caroline Bolling, Dina C. Janse van Rensburg, Ellen Casey, Naama Constantini, Nana Akua Achiaa Adom-Aboagye, Rita Tomás, Yuka Tsukahara, Carolyn A. Emery, H Paul Dijkstra, Alex Donaldson

Bibliographic record

VenueBritish Journal of Sports Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcMaster UniversityWomen and Children’s Health Research InstituteUniversity of AlbertaWestern UniversityUniversity of TorontoCentre for Addiction and Mental HealthUniversity of CalgaryArthritis Research Centre of CanadaResearch CanadaUniversity of British Columbia
FundersInternational Olympic CommitteeLa Trobe UniversityUniversity of Calgary
KeywordsGirlAthletesInjury preventionConcept mapSuicide preventionHuman factors and ergonomicsPoison control

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.074
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.004
Science and technology studies0.0060.006
Scholarly communication0.0060.006
Open science0.0030.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.043
GPT teacher head0.309
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2025
Admission routes2
Has abstractyes

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