Pregnant Pitches: Navigating Football Before and During Pregnancy
Bibliographic record
Abstract
With more high-profile footballers smashing expectations and returning to peak performance after childbirth, pregnancy is no longer seen as a barrier to elite football. In order to optimize postpartum return to performance it is essential that the football community proactively work together to obtain up to date information related to pregnancy and apply this to the context of current female football. To create scalable guidance for safe football participation during pregnancy and effective protocols for successful performance, we must first establish the current state of scientific research on the topic. Through appropriately standardized and timely player education, targeted medical screening, monitoring, and intervention, the reproductive health and fertility potential of female footballers can be optimized in support of their family planning wishes and long-term football participation and performance. The state of the science related to exercise and athletic performance in pregnancy has evolved in recent years. This evolution requires articulation and integration with the current state of female football. The bridge between these two spheres highlights the need for maintained football participation throughout pregnancy, following appropriate precautions, to reduce the risk of detraining and to help support players' psychosocial well-being and sense of athletic identity. This review articulates the state of the science as relevant to the contemporary female footballer together with their support system, to provide the needed foundation for the application of these considerations into practice. Greater support for footballer-mothers keeps more women in the game for longer, and inspires women and girls to get involved in a sport that can be continued as a lifelong endeavor.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".