Leveling the Playing Field: Key Considerations for the Female Endurance Athlete Across the Lifespan
Bibliographic record
Abstract
ABSTRACT More female athletes are participating and competing in endurance events, yet injury rates are higher than male athletes. To level the playing field, coaches need to understand the unique biophysiological considerations when working with the female athlete. Adolescent female athletes must manage puberty and menstruation while participating and performing. Pregnant athletes must adapt to changes related to the growing baby, yet balance participation and performance without significant deconditioning. Postpartum athletes must navigate return to sport along with healing from childbirth and recovering from pregnancy-related changes while also managing their mental health, pelvic health, bone health, etc. Finally, the master's female athlete must maintain performance while navigating menopause and the effects of changing hormones. Recognizing relative energy deficiency in sport, menstrual cycle abnormalities, bone health, and pelvic floor dysfunction can assist the athlete in getting referred to the appropriate health provider and prevent potential short- and long-term injuries. This evidence-based article will provide practical approaches to recognize and screen common issues affecting female athletes during the different lifespan stages and provide resources and recommendations to help the athlete to stay healthy and in the game.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".