Resources to Guide Researchers in the Pursuit of High-Quality Sport Science Research in Women
Bibliographic record
Abstract
Implementing a high-quality approach to the methodological classification and control of the ovarian hormone status of female participants in research is challenging and complex. These complexities have likely hindered the formulation of robust conclusions regarding the effects of the ovarian hormones (estrogen and progesterone) on sport science outcomes. We have therefore developed practical study design tools and resources to aid researchers in the pursuit of high-quality research in women. Specifically, this paper presents a tiered framework outlining varying levels of methodological classification and control of participant ovarian hormone status in exercise and performance studies involving postpubertal to premenopausal female participants. To support implementation, we also provide resources including a flowchart, participant prescreening questionnaire, and examples of applying the tiering system in practice. These tools will assist researchers in planning and study design that adopts appropriate classification and control of ovarian hormone status of its participants. These guides have been generated from our experiences of implementing a high-quality approach in the applied sports science setting. We discuss the balance between methodological rigor, practical constraints, participant burden, and the generalizability of findings. Ultimately, this paper provides resources to assist researchers in adopting high-quality, suitable approaches to studying female athletes, regardless of available resources thereby facilitating the correction of sex-based research biases in the literature.
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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 | 0.401 | 0.556 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".