“My biggest learning curve:” an interpretive phenomenological analysis of coaches’ experiences of working with athletes who experience a menstrual cycle
Bibliographic record
Abstract
The purpose of this interpretative phenomenological analysis was to describe and interpret coaches’ experiences working with athletes who experience a menstrual cycle (MC). Participants included 15 high-performance coaches (11 women, 4 men) involved at the national or international level in a variety of winter and summer sports. Coaches participated in online, semi-structured one-on-one interviews. Data were analysed using an interpretative phenomenological analysis approach, and coaches’ experiences are represented by four main themes: (a) ‘Make them feel safe’ – facilitating a culture of trust, empathy, and support; (b) ‘It will be different’ – recognising athletes’ unique and personal experiences; (c) ‘Find strategies that allow them to participate’ – managing training and performance; and (d) ‘Make it more normalised’ – reducing stigma, barriers, and awkwardness. Our study advances MC-research in sport by highlighting the need to take athlete-centred approaches to understand athletes’ unique MC experiences, as well as coaching-education and practices for progressing and supporting women athletes. In addition, we provide recommendations for future research and evidence-informed practices that can support athletes who experience a MC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".