National team biathletes’ experiences of the menstrual cycle: “it’s something that needs to be heard”
Bibliographic record
Abstract
Objectives: To describe national team biathletes' experiences of their menstrual cycle (MC) while training and competing, and to identify factors to be considered in the development of policy and practice to support these athletes. Methods: Participants included 18 national team biathletes (ages 17-32 years) who experience the MC. Following a qualitative description design, athletes participated in one-on-one semistructured interviews that were audio-recorded, transcribed verbatim and analysed using a content analysis process. Results: Four descriptive themes represent the findings: (a) 'A very under-rated part of performance and training': Critical impacts of the MC on sport; (b) 'It varies month to month': Fluctuation in occurrence and impact of MC symptoms; (c) 'Block out and get through it': Managing the MC for performance; and (d) 'For the next generation': Improving policy and practice around the MC. Findings from this research outline actionable steps to support athletes who experience a MC, including developing mandatory MC education, increasing knowledge about the management of MC symptoms (eg, MC tracking, leakproof suits), and creating a fair point system of the overall biathlon season ranking allowing elimination of two race results that may have been affected by a health issue, such as adverse MC symptoms. Conclusions: This research outlines the critical need for 'macro' level policies and practices that reduce the perceived impact of MC symptoms on athletes' training and performance. Furthermore, individual variations described in this study highlight the importance of individualised approaches to supporting athletes as they navigate the MC alongside the demands of sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".