Evidence-based recommendations to support elite athletes during preconception and pregnancy: A modified Delphi survey of elite Australian athletes and key stakeholders
Bibliographic record
Abstract
OBJECTIVES: Pregnancy policies have been identified as a critical support for elite athletes during preconception and pregnancy. However, many sporting organisations lack such policies and the resources to create them. This study aimed to establish consensus of evidence-based policy and practice recommendations for supporting preconception and pregnancy in Australian elite athletes. DESIGN: November 2024. METHODS: A set of draft policy and practice recommendations was developed a priori using previous research. These were reviewed in Round I and those that did not achieve consensus (≥75 % agreement with no changes proposed) or were updated based on participant feedback were put through to a subsequent round and the process was repeated for a third round. The resulting recommendations were sent to the Australian Institute of Sport for their review and endorsement. RESULTS: Twenty-two, 15 and 13 respondents completed the surveys for rounds I to III respectively. Round I achieved consensus for nine recommendations, though edits were made to all 13 recommendations. Round II achieved consensus for 11 recommendations with edits to five recommendations and Round III achieved consensus for all five remaining recommendations. The final recommendations were reviewed by the Australian Institute of Sport and endorsed following minor revisions. CONCLUSIONS: This modified Delphi study achieved consensus on evidence-based recommendations to support preconception and pregnancy in Australian elite athletes, offering a comprehensive framework for sporting organisations to adopt and adapt when developing future pregnancy policies.
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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 | 0.136 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".