Elite Female Distance Runners and Advice During Pregnancy: Sources, Content, and Trust
Bibliographic record
Abstract
More elite female distance runners are opting to have children during their athletic careers. Despite this, there is a dearth of information regarding pregnancy and physical activity for elite level athletes. Further, current pregnancy physical activity guidelines are not relevant for this population`s needs. Two research questions frame this study: are elite female distance runners’ pregnancy informational needs being met?; where do they seek and find trustworthy advice on physical activity during pregnancy? Open-ended, semistructured interviews were conducted with 14 women who experienced at least one pregnancy within the past five years, had achieved a minimum of the USA Track and Field 2012 Olympic Team marathon trials ‘B’ entry standard or equivalent performances for distance running events 1,500m or longer. The participants had between one—three children, hail from five countries and participated in 14 Olympic Games and 72 World Championships. Utilizing poststructuralist feminist theory and thematic analysis, our findings revealed that the participants received advice from three main sources, both in person and online: medical professionals, coaches, and other elite female distance runners. However, we found that they also received unsolicited advice and comments from community members where they lived. The participants identified fellow elite female distance runners as the most reliable and trustworthy sources of information, followed by medical professionals, then coaches. Ultimately, the women revealed a lack of formal sources they could turn to for trustworthy advice about how to have a safe and healthy pregnancy while continuing to train at a high intensity. These results illuminate the need to meet female elite athletes’ informational needs in terms of well-being during pregnancy.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".