Menstrual disorder and associated factors among medical students in Uganda: a cross-sectional study
Bibliographic record
Abstract
Menstrual disorders (MD) affect 75% of women worldwide and account for the majority of morbidity in women of childbearing age. Heavy and prolonged menstruation are linked to severe anemia and its sequelae, irregular menstruation predisposes females to unwanted pregnancies, which can result in unsafe abortion with associated maternal injuries and even death. Furthermore, students suffering from PMS, dysmenorrhea, or heavy menstruation are more likely to miss lectures and exams. We determined the prevalence and factors associated with menstrual disorders among undergraduate medical students at Mbarara University of Science and Technology (MUST) in southwestern Uganda. We conducted a cross-sectional study in the Faculty of Medicine (FOM) at MUST from 2nd October, 2023 to 1st November, 2023 using consecutive sampling among the female undergraduate students, We included students from 19 to 45 years and excluded those who were either pregnant or breastfeeding. Socio-demographic, gynecological, lifestyle and clinical data were obtained through self-administered questionnaires. The prevalence of menstrual disorders was the proportion of participants with any of dysmenorrhea, Premenstrual Syndrome (PMS), infrequent, frequent, prolonged, heavy or irregular Menstruation. Modified Poisson regression analysis was used to evaluate associations between menstrual disorders and independent variables. A total of 290 students were enrolled with mean age 23.93 (± 4.40) years. The prevalence of menstrual disorders was 85.6% (249/290) (95%CI 81.83–89.90), indicates that these issues may be a common concern for young women, especially those in academic environments. Having stress [aPR 1.13, 95% CI (1.03–1.24), p = 0.007], being below 24 years [aPR 1.12, 95% CI (1.01–1.25), p = 0.034] and being underweight [aPR 1.15, 95% CI (1.08–1.22), p < 0.001] were independently associated with Menstrual disorders. More than three-quarters of medical students at MUST experience menstrual disorders, highlighting a significant health concern in this population. These findings emphasize the need for integrating menstrual health education and stress management programs into medical curricula. Health policies should prioritize screening and management of menstrual disorders, with recommendations for wellness programs such as yoga, sports, and creative activities to support student well-being.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".