Female Athletes Report Positive Experiences as Research Participants
Bibliographic record
Abstract
Given the underrepresentation of women in sports and exercise science research, we sought to understand the experiences of female athletes currently involved in applied sports and exercise science research to inform future studies and potentially increase participation rates. Accordingly, we investigated the experiences of 89 female athletes (n = 48 cyclists/triathletes, n = 19 race walkers, n = 22 National Rugby League Indigenous Women's Academy players) who participated in four separate studies of sports performance with different methodological characteristics. Participants completed a questionnaire upon study completion that queried prior research participation, reasons for participating and experiences during the current study. Across all 89 athletes, 81% were first-time research participants, with the primary barriers cited as a perceived lack of opportunities or being unaware of opportunities (93%). Participants rated an interest in the research outcome as the most important aspect influencing their decision to participate (90 ± 14 [out of 100]), followed by the opportunities to receive personalized results (84 ± 20) and education (78 ± 27). Most participants (87%) stated that they would apply the study findings to their sports involvement, while the remaining 13% reported that they required support to understand the application of results. The majority (94%) of participants indicated a willingness to participate in future studies, while the research experience was rated positively at a mean 77 out of 100. Ultimately, our findings uncovered a perceived lack of opportunity as the primary barrier to female athlete research participation. As such, opportunities for women to participate in high-quality studies should be prioritized.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".