“My Biggest Learning Curve:” Coaches’ Experiences of Working with Athletes who Menstruate
Bibliographic record
Abstract
Menstruation is experienced by many athletes who participate in sport, yet it is a topic that is still considered “taboo” and rarely addressed in sport, academic, or broader public discourse. Low levels of coach knowledge regarding menstruation in sport can hinder athlete-coach communication (Höök et al., 2021), and coaches have voiced interest in trying to understand how to communicate with athletes about their menstrual cycle and associated symptoms (Clarke et al., 2021). The objective of our program of research is to identify necessary components for developing evidence-based practices and guidelines to support athletes who menstruate. The specific purpose of this study was to describe coaches’ experiences of working with athletes who menstruate. Participants included 15 high-performance coaches (11 women, 4 men) involved at national and international levels in a variety of winter and summer sports. Using a qualitative description study design, participants engaged in one-on-one semi-structured interviews via Zoom. Interviews were audio-recorded, transcribed verbatim, and analyzed using a process of content analysis as described by Elo and Kyngäs (2008). The findings of this study are represented by one overarching theme: managing athletes’ menstruation experiences, and four main themes: (a) understanding symptoms and contraceptives, (b) normalizing conversations, (c) establishing coaching education, and (d) creating change. The complex and diverse experiences shared by participants highlight the importance of studying menstruation within the sport context. Findings from this research may help to inform the development and implementation of sport practices and guidelines that support menstruating athletes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".