Filling the evidence void: exploration of coach and healthcare provider experiences working with pregnant and postpartum elite athletes – a qualitative study
Bibliographic record
Abstract
OBJECTIVE: Recent research grounded in the experiences of elite female athletes has shed light on the complex challenges of navigating sport environments that do not support or value pregnant or postpartum athletes. The purpose of this study was to explore the unique experiences of coaches and healthcare providers working with pregnant and postpartum elite athletes, and to identify actionable steps for research, policy and culture change to support them. METHODS: Sixteen participants (five coaches, three physicians and eight physiotherapists), who have worked with pregnant and/or postpartum elite athletes within the last 5 years, participated in this qualitative study. Thirteen participants self-identified as women, and three as men. Data were generated via semistructured one-on-one interviews that were audiorecorded, transcribed verbatim and analysed through a process of content analysis. RESULTS: The findings of this research are represented by five main themes: (a) lack of female athlete reproductive research, (b) need for evidence-informed education and training, (c) need to develop evidence-based progression for sport participation in pregnancy and postpartum, (d) open communication to support athlete-centred care and (e) essential supports and changes required for pregnant/postpartum athletes. CONCLUSION: Findings from this study, which are grounded in the unique perspectives of coaches and healthcare providers, outline specific recommendations to inform policy and practices that support athletes through the perinatal period, such as developing evidence-based return-to-sport protocols.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".