Association of Psychologic and Nonpsychologic Factors With Primary Dysmenorrhea
Bibliographic record
Abstract
BACKGROUND: Primary dysmenorrhea seems to be one the most common gynecologic condition in women of childbearing age. OBJECTIVES: The aim of this research was to evaluate psychologic and nonpsychologic risk factors of primary dysmenorrhea. MATERIALS AND METHODS: A cross-sectional study was conducted on medical sciences students of Babol University of Medical Sciences. In this study, 180 females with dysmenorrhea and 180 females without dysmenorrhea were enrolled. Psychological risk factors were evaluated in four domains including affect, social support, personality, and alexithymia. Four questionnaires were used to assessed aforementioned domains, namely, Social Support Questionnaire (SSQ), depression, anxiety, stress (DAS-21), 20-item Toronto Alexithymia Scale (TAS-20), and NEO-Five Factor Inventory of Personality (NEO-FFI). In addition, nonpsychologic factors were evaluated in three domains including demographic characteristics, habits, and gynecologic factors. Data were analyzed using the χ2 test and multiple logistic regression analysis. RESULTS: The strongest predictor of primary dysmenorrhea was low social support (OR = 4.25; 95% CI, 2.43-7.41). Risk of dysmenorrhea was approximately 3.3 times higher in women with alexithymia (OR = 3.26; 95% CI, 1.88-5.62), 3.1 times higher in women with menstrual bleeding duration ≥ 7 days (OR = 3.06; 95% CI, 1.73-5.41), 2.5 times higher in women with a neurotic character (OR = 2.53; 95% CI, 1.42-4.50), 2.4 times higher in women with a family history of dysmenorrhea (OR = 2.43; 95% CI, 1.42-4.50), and twice higher in women with high caffeine intake (OR = 1.97; 95% CI, 1.09-3.59). CONCLUSIONS: Low social support, alexithymia, neuroticism trait, long menstrual bleeding, family history of dysmenorrhea, and high-caffeine diet are important risk factors for women with primary dysmenorrhea. This study recommended considering psychologic factors as an adjuvant to medical risks in evaluation and treatment of primary dysmenorrhea.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".