Is your system fit for purpose? Female athlete health considerations for rugby injury and illness surveillance systems
Bibliographic record
Abstract
This review discusses female-specific health considerations in injury and illness surveillance and provides rugby-specific recommendations for future surveillance. Identifying priority injury and illness problems by determining those problems with the highest rates within women's rugby may highlight different priorities than sex comparisons between men's and women's rugby. Whilst sports exposure is the primary risk for health problems in sports injury and illness surveillance, female athletes have health domains that should also be considered. Alongside female athlete health domains, studies investigating rugby injuries and illnesses highlight the need to broaden the health problem definition typically used in rugby injury and illness surveillance. Using a non-time-loss health problem definition, recording female-specific population characteristics, embedding female athlete health domains and having up-to-date injury and illness coding systems should be prioritized within surveillance systems to begin to shed light on potential interactions between sports exposure, health domains and, injuries and illnesses. We call for a collaborative approach across women's rugby to facilitate large injury and illness datasets to be generated and enable granular level categorization and analysis, which may be necessary for certain female athlete health domains. Applying these recommendations will ensure injury and illness surveillance systems improve risk identification and better inform injury and illness prevention strategies in women's rugby.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".