Comparative Efficacy of Mindful Self-Compassion Therapy and Acceptance and Commitment Therapy on Alexithymia and Pain Anxiety in Employed Women with Migraine
Bibliographic record
Abstract
Introduction: Migraine headaches, as a neurogenic disorder, significantly impact patients’ psychological well-being. This study aimed to compare the effectiveness of Mindful Self-Compassion Therapy (MSCT) and Acceptance and Commitment Therapy (ACT) in addressing alexithymia and pain anxiety among employed women with migraine.Methods: This study employed a quasi-experimental design with a pretest-posttest framework with a control group. The target population comprised employed women suffering from migraine headaches in Rasht (Iran), in 2023. A purposive sampling method was used to select 60 participants, who were then randomly allocated into three equal groups (n=20 per group, two intervention groups, and one control group). Assessment tools included the Toronto Alexithymia Scale (TAS-20), measuring difficulty identifying feelings, difficulty describing feelings, and externally oriented thinking, and the Pain Anxiety Symptoms Scale (PASS-20). Both intervention groups received eight 90-minute sessions of their respective therapies. Data were analyzed using multivariate analysis of covariance (MANCOVA) in SPSS software (version 26).Results: After controlling for pretest scores, significant between-group differences emerged in posttest measures of alexithymia components (difficulty identifying emotions, difficulty describing emotions, externally oriented thinking) and pain anxiety. However, no significant differences (P>0.05) were observed between the MSCT and ACT groups’ outcomes at post-test.Conclusion: Both MSCT and ACT demonstrated comparable efficacy in mitigating the psychological consequences of migraine, particularly in reducing alexithymia and pain anxiety.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".