472 BO42 – Are we asking the right questions? Exploring female athlete perceptions on important menstrual cycle topics
Bibliographic record
Abstract
Background Menstrual cycle (MC) research in sport is rapidly increasing. Considering end-user perspectives is critical when addressing this fundamental health process to ensure practical outcomes related to athletes’ training and performance. Objective To understand what athletes perceive is important to consider regarding their menstrual cycles in sport. Design Qualitative research study; one-on-one semi-structured interviews. Setting Online; Zoom audio and video. Participants Twenty high-level team and individual sport female athletes competing nationally (65%) or internationally (35%). Assessment of Risk Factors Participants discussed their MC experiences as athletes in response to 12 interview prompts. Main Outcomes MC-related topics for consideration by researchers and healthcare practitioners were determined through inductive, grounded-theory analyses using line-by-line coding, focused coding, and theory building. Results MC topics were highlighted within four overarching theories: 1) Training and performance, 2) Culture, 3) Health, and 4) Knowledge. Athletes’ feelings related to support in their athletic environment were explained by suggesting strategies they desire from their coaching and support staff. Participants wanted access to modifications and symptom management strategies as they strived to understand how to train around their cycle to optimize performance. The athletes recognized the MC should be individualized, however they desired normalizing the cycle as a health process within sport. This included recognizing relationships with overtraining, body image, and misdiagnoses. Hormonal contraceptives were discussed, and athletes had a strong desire to be better informed. Misinformation was evident despite knowledge accrual through family members, peers, and technology. Participants recognized that access to education would be key to increased MC knowledge and awareness. Conclusions These data provide practical recommendations for coaches and practitioners to support female athlete health and wellness. Researchers should continue to pursue high-quality MC research on training strategies, performance impacts, injury risk, and hormonal contraceptives. Sport organizations should prioritize MC education for athletes and coaches.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".