Sport and exercise medicine/physiotherapy publishing has a gender/sex equity problem: we need action now!
Bibliographic record
Abstract
OBJECTIVES: We aimed to determine (1) the proportion of women authors overall, in first (lead) and last (senior) author positions, (2) the proportion of women research participants and (3) the association between women in first and/or last author positions and the proportion of women research participants in original research articles and editorials/opinion pieces in four sport and exercise medicine/physiotherapy journals. METHODS: .We reviewed all original research articles and editorials/opinion pieces published in 2008, 2009, 2018 and 2019. For each, we aimed to determine the gender/sex of all authors (through gender pronouns, Google Scholar, ResearchGate, institutional profiles, personal websites, photographs and/or social media), and the gender/sex of study participants reported as 'female' or 'male' or 'women' or 'men' or 'girls' or 'boys'. RESULTS: We included 952 original studies and 219 editorials/opinion pieces. There were 5146 authors of original studies and 706 authors of editorials/opinion pieces. Compared with 2008/2009, the proportion of women as first and last authors was 3.6% (33.0% compared with 29.4%) and 4.8% (33.2% compared with 27.4%) higher respectively in 2018/2019. On average, the proportion of women participants in original studies remained largely unchanged over the 10-year period, only 10% of all participants were women in studies. CONCLUSION: Women are strikingly under-represented in first and last author positions, as are women participants in sports and exercise medicine/physiotherapy journals.
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 | 0.132 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".