Pushing for change: a qualitative study of the experiences of elite athletes during pregnancy
Bibliographic record
Abstract
OBJECTIVES: Athletes train and compete at the elite level during their reproductive years, yet sport policies that support pregnant athletes are lacking. The experiences of elite athletes during pregnancy are vastly under-represented, and such voices are needed to support evidence-informed policy. Thus, the purpose of this qualitative study was to describe the experiences of elite female athletes as they navigate pregnancy, and to identify sport policy considerations regarding participation during pregnancy. METHODS: Twenty athletes (mean age 35±5 years) who, within the last 5 years, had trained or competed at the elite level immediately prior to becoming pregnant were included. Data were generated via one-on-one semistructured interviews that were audio-recorded, transcribed verbatim and analysed through a process of content analysis. RESULTS: The findings of this study are represented by one overarching message: mother versus athlete, and five main themes: (1) pregnancy planning and fertility, (2) pregnancy disclosure and discrimination, (3) training pregnant athletic bodies, (4) safety concerns, and (5) supportive network and equitable funding. CONCLUSION: In-depth stories shared by participants highlight the many significant decisions athletes must make as they navigate pregnancy alongside elite sport participation. The shared experiences of pregnant athletes highlight clear challenges that should be considered in the development of sport policy and practices that are inclusive and supportive of female 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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".