488 FO06 – Are female athlete health needs being assessed and addressed in preparticipation examinations? A scoping review
Bibliographic record
Abstract
Background Preparticipation examinations (PPEs) are routinely used to collect an athlete’s baseline health information prior to the start of a competitive season. However, a lack of female-specific health related questions could result in missed red flags and subsequent injury or illness. Objectives Design/Setting Five databases were searched; Embase, Scopus, CINAHL, Medline Ovid, SPORTDiscus. Studies with female athlete specific questionnaires/recommendations were included in the scoping review. Three reviewers independently screened titles and abstracts, followed by full text for eligibility and data extraction with conflicts being resolved by a third-party reviewer. Extracted data was grouped into pre-determined categories and mapped against current IOC extension health domains. Results 1351 studies were screened and 53 were deemed eligible and included. Thirty-five studies included relevant female-specific questions, and an additional 18 had recommendations and/or screening tools only. Of the female-specific questions, ass PPEs had questions related to menstrual health. Twenty-three (66%) had questions concerning disordered eating/eating habits. Twenty-five studies (71%) included questions on body weight/image, 15 (43%) referred to musculoskeletal/bone health and only three (9%) included questions surrounding mental health. Only seven (20%) had questions in four or more domains. Conclusions There is currently as gap in female specific health questions being included in PPEs which could have significant health and optimal participation implications. A more comprehensive, standardized PPE with the focus on inclusion of female specific questions and considerations should be developed to improve health and optimal participation of female athletes around the world
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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 | 0.022 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".