Uncovering Sleep Behaviour in Women’s Football: What Evidence Do We Have?
Bibliographic record
Abstract
Sleep plays an important role in performance, health and well-being, yet may represent a challenge to many female football players. Areas of the brain that are involved in the regulation of sleep contain receptors for the ovarian hormones, estrogen and progesterone. While limited evidence exists describing sleep across the menstrual cycle in elite female football players, related data suggest that female athletes may report poor subjective sleep, despite appropriate objectively measured sleep quality and quantity, particularly prior to or during menstruation. Some of the precipitators of poor sleep in female athletes may include: travel and jetlag, caffeine consumption, light exposure, competing at night, menstrual cycle symptoms, menstrual cycle dysfunction, low iron status and performing caring responsibilities. This article discusses potential approaches to protect, assess and provide interventions to support sleep in female football players. Despite the evidence base of research being predominantly studies of male athletes, there are a number of specific recommendations that can be made for female athletes. These include advice regarding methods to assess sleep and provide interventions based on resource availability, monitoring and managing menstrual cycle symptoms and menstrual dysfunction, and consideration of mitigating strategies to reduce the effects on known sleep disruptors. Many female footballers navigate unique challenges related to sleep; however, with appropriate support from coaches and sport science and sports medicine practitioners, an appropriate support network can be provided to not only optimise performance, but the physical and mental health 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 | 0.029 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".