The Effectiveness of Acceptance and Commitment Therapy on Alexithymia and Adherence to Treatment in women with Type 2 Diabetes
Bibliographic record
Abstract
Background: Diabetes is the most common metabolic disease in the world that affects both physical and mental health.Therefore, it is necessary to take effective measures to reduce the physical and psychological problems of women with diabetes.This study aimed to determine the effectiveness of acceptance and commitment therapy on alexithymia and adherence to treatment in women with type 2 diabetes.Methods: This was a quasi-experimental study with a pre-test-post-test and follow-up design with a control group.Among women with type 2 diabetes who were referred to the Golhay Comprehensive Health Center in Islamshahr in 2023, thirty people were selected through convenience sampling.They were randomly assigned to two experimental and control groups (15 people in each group).Data collection tools in the pre-test, post-test, and follow-up stages were the Toronto Alexithymia Scale and the Medanlou Adherence to Treatment Questionnaire.The experimental group underwent a group intervention based on Ross Harris Acceptance and Commitment Therapy for 8 sessions of 1.5 hours each (one session per week).Data analysis was performed using analysis of covariance and repeated analysis of variance using SPSS-26 software.Results: Based on the results, the variables of alexithymia (F=9.41,p<0.01) and adherence to treatment (F=10.09,p<0.01) were statistically different in the experimental and control groups.Furthermore, the results of repeated measures analysis of variance indicated that the effectiveness of treatment method was sustained on both variables measured after two months of treatment (p<0.01).Conclusion: Acceptance and commitment therapy has reduced alexithymia and increased adherence to treatment in the study population.Therefore, this therapeutic intervention can be used alongside other pharmacological and nonpharmacological treatments in women with type 2 diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".