It is time to improve our research design, reporting and interpretation of sex and gender in exercise science and sports medicine research
Bibliographic record
Abstract
Historically, much of the research in exercise science and sports medicine has collected, reported, and discussed sex and gender as synonymous and interchangeable.This conflation has hindered scientific progress, limiting our understanding and potentially reinforcing social biases through poorly framed research questions, flawed methodological designs, and misinterpretations of findings related to sex and gender.Although there are multiple variations in definitions utilised, broadly, gender is a social construct and sex is a biological construct.While sex and gender are intertwined, they can act separately, and they most often act interactively to influence the efficacy of interventions and outcomes.To advance the field, research must explicitly address sex and/or gender at each stage of the research process, at design, reporting, and interpretation as recommended in other health and medicine fields.The current editorial provides a step-by-step process to guide progress in this area (table 1).Running head: It is time to improve our research design, reporting, and interpretation of sex and gender in exercise science and sports medicine research
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 | 0.102 | 0.402 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 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".