A scoping review of identifying research on menstruation and menstrual cycle among female athletes of low and low-and-middle-income countries
Bibliographic record
Abstract
BACKGROUND: Menstruation involve intricate physiological, social, cultural, religious, and psychological factors that deeply impact women and girls globally. Disparities in access to menstrual products, facilities, information, and social support are evident between high-income countries (HICs) and low-income countries (LICs). Female athletes in low- and middle-income countries (LMICs) encounter distinct challenges due to limited research on the significant effects of menstruation on their daily lives as well as athletic performance. This scoping review investigates existing research on menstruation's influence on female athletes in LMICs, proposes research gaps, and propose future study directions. METHOD: We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-Ext) guidelines. We systematically searched multiple databases, including PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and Google Scholar, to identify peer-reviewed publications, grey literature, and relevant sources. Our inclusion criteria required articles to contain at least one performance-related parameter related to menstruation or explore at least one menstrual phase among LMICs and LICs athletes. RESULT: Our initial search yielded 1490, of which 88 potential articles were considered after title and abstract screening. After duplicate screening, 26 studies met our inclusion criteria. Eighteen studies employed a cross-sectional research design from adolescence to the 30s. Ten studies specifically focused on investigating the menstrual cycle (MC) and its impact on athletes, while five articles focused on physiology and six examined both physiology and the MC. Eight studies explored performance, while two reported on the intersection of performance. CONCLUSION: The research on female athletes from LMICs requires a more consistent focus on menstruation's impact on athletes' well-being and performance. This scoping review underscores the urgent need for more in-depth, longitudinal, and quantifiable research that explicitly emphasizes the unique needs of athletes from LMICs. By gaining deeper insights into their experiences, barriers, and impact on performance and health, we can enhance these athletes' overall well-being and broader social, cultural, and gender equality agendas.
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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 | 0.024 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".