Comparison of Menstruation-Related Symptoms Before and During Menstruation of University Students in Japan, a Year after the COVID-19 Pandemic
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic affected the daily lifestyle of people, including many aspects affecting young women. Subsequent to the COVID-19 pandemic stress and anxiety have been reported related to menstrual disorders (Takmaz, Gundogmus, Okten, & Gunduz, 2021). The purpose of this study was to investigate the intensity and to compare menstruation-related symptoms before and during menstruation among university students in Japan. We conducted an online cross-sectional study from May to July 2021 using a menstrual distress questionnaire (MDQ) to assess symptoms experienced before and during menstruation. Our results showed that of 141 students, five students (3.5%) did not report any symptoms before menstruation and one student (0.7%) had no symptoms during menstruation. We found that the most frequently experienced symptoms before menstruation were skin blemishes or disorder, mood swings, irritability, swelling, cramps, fatigue, take naps, stay in bed, feeling sad or blue, weight gain and difficulty concentrating. The most frequently experienced symptoms during menstruation were cramps, fatigue, irritability, mood swings, take naps, stay in bed, feeling sad or blue, backache, swelling, skin blemish or disorder, and poor school/work performance. The total MDQ score was significantly higher during menstruation than before menstruation. Among the MDQ eight scales, the scores of five scales including pain, autonomic reaction, impaired concentration, behavior change, and control were significantly higher during menstruation than before menstruation. The prevalence of increased appetite and craving for sweets were higher than MDQ symptoms before menstruation. The prolonged exposure to pandemic may have more effect on psychological symptoms than on physical symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".