Efficiency Comparison of Behavioral Activation and Acceptance-Commitment Therapy on the Alexithymia in Patients with Diabetes Type 2
Bibliographic record
Abstract
The primary objective of the present study was to compare the effectiveness of two therapeutic approaches, namely Behavioral Activation (B.A) and Acceptance-Commitment Therapy, in addressing alexithymia among individuals with Type 2 diabetes. The research methodology employed was experimental, employing a pre-test-post-test design with an equivalent control group. The study encompassed individuals diagnosed with Type 2 diabetes residing in Abadan and Khorramshahr cities (Iran). A sample of 45 individuals with Type 2 diabetes was selected using a convenience sampling method. Subsequently, from this sample, 15 participants were randomly allocated to Experimental Group 1 (receiving Behavioral Activation therapy), another 15 to Experimental Group 2 (receiving Acceptance-Commitment therapy), and the remaining 15 to the Control Group. The research utilized the Toronto Alexithymia Scale Test as the assessment tool. Data analysis was conducted using multivariate and univariate analysis of covariance. The results of the data analysis revealed a significant distinction in the impact of Behavioral Activation (B.A) and Acceptance-Commitment therapy on the reduction of alexithymia in individuals with Type 2 diabetes. Notably, Acceptance-Commitment therapy exhibited a higher level of effectiveness in reducing alexithymia compared to Behavioral Activation (B.A) among individuals with Type 2 diabetes. Consequently, it can be concluded that Acceptance-Commitment therapy, in conjunction with its acceptance commitment techniques, is a more efficacious approach for reducing alexithymia in individuals diagnosed 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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".