Predicting the Premenstrual Syndrome Based on Alexithymia and Self-Efficacy in Women with Migraine: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Premenstrual Syndrome (PMS) is a common problem in women with migraines. Due to the importance of recognizing aspects of this issue, this study was conducted to investigate the role of alexithymia and self-efficacy factors in predicting PMS. Methods: This analytical cross-sectional study was performed on the statistical population of women with migraine referred to medical centers in Rasht in 2021. 160 women with migraines participated in convenience sampling methods from medical centers and responded to the Demographic Information Questionnaire, Premenstrual Symptoms Screening Tool (PSST), Toronto Alexithymia Scale (TAS-20) and General Self-Efficacy Scale (GSE). Data analysis was performed using IBM SPSS 21 (IBM Inc, New York, USA) statistical software. Results: The results showed that 59.6% of the women had PMS. Pearson correlation coefficient showed that PMS was negatively associated with self-efficacy (r=-0.28; p=0.001) and positively associated with alexithymia (r=0.22; P=0.001). Multiple linear regression analysis indicated that the self-efficacy variable (β=-0.27) negatively predicts 11% of the changes in the PMS variable. Conclusion: Self-efficacy and alexithymia are PMS-related factors; thus it is suggested that health care providers pay attention to the importance of these psychological factors in developing treatment plans.
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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 | 0.000 |
| 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) | 0.001 | 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".