Female, woman and/or girl Athlete Injury pRevention (FAIR) practical recommendations: International Olympic Committee (IOC) consensus meeting held in Lausanne, Switzerland, 2025
Bibliographic record
Abstract
) International Olympic Committee Consensus meeting was held from 31 March to 2 April 2025 (Lausanne, Switzerland).The FAIR Consensus followed an eight-step hybrid method. 109 authors from six continents conducted: (1) systematic reviews synthesising evidence on injury prevention strategies and modifiable risk factors for lower-extremity and upper-extremity injuries, concussions and spine/chest/abdominal/pelvic injuries/pain, (2) a scoping review synthesising dissemination and implementation (D&I) approaches; and (3) a concept mapping project generating knowledge on gender/sex-related factors for injury prevention. These projects underpinned draft recommendations subsequently voted on by a steering committee (n=24) and an external advisory committee chair over two anonymous survey rounds. Recommendations, Round 1 voting results and suggestions/dissenting comments were discussed between Round 1 and 2 voting. Consensus was defined as 'critical to include' (≥70% scored recommendation as 7-9 (9-point Likert scale, 1=not important; 9=critically important) AND ≤15% scored recommendation as 1-3).The 56 FAIR recommendations address: primary injury prevention (n=16) (policy/rules/laws=6; personal protective equipment=8; training=2); secondary injury prevention (n=4); modifiable risk factors (n=12); approaches to D&I (n=14); and promoting gender/sex-supportive environments (n=10).The FAIR Consensus informs evidence-based best practices and policy for injury prevention, approaches to implementation and creation of supportive environments for female/woman/girl athletes. Every person at all levels of sport can, and should, take responsibility for actions that positively influence female/woman/girl athlete health and safety.
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 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 | 0.065 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".