“Everyone was sort of just like ‘ew’”: Adolescent athletes’ experiences of menstruation in sport
Bibliographic record
Abstract
Young girls often avoid being physically active due to physical and psychological symptoms of menstruation. Therefore, the purpose of the present study was to explore youth athletes’ experiences of menstruation in sport. Seven female athletes between 16 and 18 years old took part in semi-structured interviews. Interpretive Description methodology (Thorne, Citation2016) was used, and interview transcripts were analyzed using reflexive thematic analysis (Braun & Clarke, Citation2006, Citation2022). Athletes experienced several physical and emotional symptoms throughout the menstrual cycle which were perceived to impact performance. Overall, athletes felt there was not enough support available for dealing with menstruation due to stigma and lack of education. This also influenced coach-athlete communication, along with other factors, such as coach gender, age, and coach-athlete relationship. Moving forward, young athletes would like more support in the form of education for athletes and coaches, and more representation and destigmatization of the topic of menstruation.Lay Summary: This study explored adolescent athletes’ experiences of menstruation in sport. Following interviews with seven female athletes, we found that athletes faced a variety of challenges when dealing with their menstrual cycle in sport, but not enough support was provided due to stigma and a lack of education.
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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 | 0.003 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".