Personality Traits and Primarydysmenorrhea: A Cross-Sectional Study in Iranian Medical Students
Bibliographic record
Abstract
Background & Objective: Dysmenorrhea is a common problem and sometimes disabling condition among women of childbearing age. It plays several problems in personal and social life. Although few studies have explored the relationship between psychosocial factors and dysmenorrhea, the evidence for a psychological etiology is growing. The aim of this research was to find and explore a relation between psychological traits and primary dysmenorrhea. Methods: A cross-sectional study was conducted with two groups of medical students (medicine and paramedicine) of Babol University of Medical Sciences. Two hundred female students participated in the study (100 dysmenorrhea, 100 without dysmenorrhea).All subjects asked to complete 20-item Toronto Alexithymia Scale (TAS-20) with three subscales (difficulties in identifying feelings, difficulties in describing feelings, externally oriented thinking)and NEO-Five Factor Inventory of Personality (NEO-FFI) with five subscales(neuroticism, extraversion, openness to experience, agreeableness and conscientiousness).The T-test and multiple linear regression was used to analyze the data. Results: Multiple linear regression analysis after adjusting for age, age menarche, family history of dysmenorrhea, menstrual status and residence area showed that individual with dysmenorrhea had significantly more than non- dysmenorrhea in some personality traits scores; neuroticism ( =4.48,p<0. 01), DIF ( =3.38,p<0.01) and total alexithymia score ( =5.65 P <0.01). Conclusion: Our resultsshowed that dysmenorrhea is characterized by increased neuroticism and alexithymiain. This study proposes that women with primary dysmenorrhea should be evaluated and treated by two departments of gynecology and psychiatry.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".