Comparative Effects of Emotion-Focused Therapy and Yoga Therapy on Somatic Symptoms and Alexithymia in Depressed Married Women
Bibliographic record
Abstract
Objective: This study investigates the comparative effectiveness of EFT and yoga therapy on somatic symptoms and alexithymia in depressed married women. Methods and Materials: This semi-experimental study employed a pre-test and post-test design with experimental and control groups. A sample of 30 married women aged 20–45 diagnosed with depressive disorder was randomly assigned to three groups: EFT (n = 10), yoga therapy (n = 10), and a control group (n = 10). Participants completed the Beck Depression Inventory, the Paul and Enright Somatic Symptoms Questionnaire, and the Toronto Alexithymia Scale. Interventions were conducted over 12 weekly 90-minute sessions. Data were analyzed using repeated measure analysis of variance and SPSS.16. Findings: EFT showed greater effectiveness in reducing alexithymia (mean reduction: 39.9%) compared to yoga therapy (27.5%) and the control group (2.1%) (p<0.001). Similarly, somatic symptoms decreased significantly in the EFT group (30.1%) compared to yoga therapy (26.8%) and the control group (0.7%). Conclusion: EFT demonstrates superior effectiveness in addressing both somatic symptoms and alexithymia in depressed married women. Yoga therapy also provides significant benefits, making both approaches valuable tools for counselors. Integrating these therapies into counseling programs may enhance psychological and marital well-being.
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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 | 0.000 |
| 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 | 0.000 |
| 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".