Under-representation of women is alive and well in sport and exercise medicine: what it looks like and what we can do about it
Bibliographic record
Abstract
Despite constituting approximately 50% of the population, women specifically are under-represented in sport and exercise medicine (SEM) and they often experience a negative bias. Our authorship group has recognised this issue based on evidence from recent studies, personal experiences and the experiences of the wider SEM community. We understand that this is a complex issue. Through this editorial, we aim to briefly highlight the issue of insufficient representation of women in SEM, discuss some of the impacts of this inadequate inclusion and other negative aspects experienced and suggest steps that we can all take to address female under-representation to improve the field of SEM
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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 | 0.027 | 0.098 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.035 | 0.033 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".