Alexithymia and Self-Efficacy With Pain Perception in Women with Migraines: A Cross-sectional Study
Bibliographic record
Abstract
Background: Pain perception in individuals with migraine is very important and is influenced by various factors. The aim of this study was to investigate the role of alexithymia and self-efficacy with pain perception in women with migraine. Materials & Methods: This cross-sectional study was performed in women with migraine referred to medical centers in Rasht in 2021. Using convenience sampling method, 160 women with migraines participated in the study and answered the demographic information questionnaire, Toronto Alexithymia Scale (TAS-20), General Self-Efficacy Scale (GSE) and McGill Pain Questionnaire (MPQ). Data was analyzed using Pearson correlation coefficient and linear regression model. Results: Of total, 152 women responded to the questionnaire (response rate= 95%). The mean age of study participants was 32.86±8.5 years. Pain perception was negatively associated with selfefficacy (r=-0.28; P=0.001) and positively associated with alexithymia (r= 0.20; P=0.001). The results of linear regression also showed that self-efficacy and Externally-Oriented Thinking (EOT) subscale explained 13% of the variance in pain perception. Conclusion: Self-efficacy and externally-oriented thinking were significant contributors of pain perception in women with migraine. These variables can be considered for adopting coping strategies in patients experiencing migraine pain.
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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 | 0.000 | 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".