Prevalence of Premenstrual Syndrome and Premenstrual Dysphoric Disorder among Mongolian College Students
Bibliographic record
Abstract
Background & aims: This study aimed to determine the prevalence of premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) and to investigate the association between premenstrual symptoms and dysmenorrhea among college students in Mongolia. Methods: A descriptive cross-sectional design was used to examine 593 women attending the School of Nursing at the Mongolian National University of Medical Science. A final sample of 572 questionnaires was used for data analysis. In addition to collecting demographic characteristics, we used the Premenstrual Symptoms Questionnaire (PSQ) to measure the degree of severity of premenstrual symptoms. Data were analyzed using descriptive statistics, Fisher’s exact test, and Cochran-Armitage trend test. Results: The findings showed that the prevalence of moderate to severe PMS and PMDD among Mongolian college students was 23.8％ and 4.7％, respectively. The most frequent symptoms were “fatigue or lack of energy” (86.7％), “anger or irritability” (80.9％), “tearfulness” (76.2％), “difficulty concentrating” (75.3％), and “anxiety or tension” (73.4％). A statistically significant association was found between dysmenorrhea and the severity of PMS/PMDD. Conclusion: The prevalence rate of PMS and premenstrual symptoms among Mongolian female college students was comparably higher than in other countries, while the prevalence of PMDD was comparable among both Western and Asian participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".