Effectiveness of compassion-focused therapy in alexithymia, adaptive behavior, treatment adherence, and biological factors in patients with type 2 diabetes
Bibliographic record
Abstract
Background and Objective: The diabetic patients experience a plethora of emotional problems besides physiological physical problems, which can affect the course of their disease. Therefore, the purpose of the present study was to investigate the effectiveness of compassion focused therapy (CFT) on alexithymia, adaptive behavior, treatment adherence, and Blood sugarof patients with type 2 diabetes. Materials and Methods: In this research, a quasi-experimental design with pretest-posttest and control group was adopted. The study population includes patients with type 2 diabetes in Ahvaz in 2020 that referred to my center. The sample size (n=30) was selected using availability sampling method, who were randomly and equally assigned to experimental (CFT), and control group. To collect research data, the Toronto Alexithymia Scale, the Psychosocial Adjustment with Illness Scale, the Treatment Adherence Questionnaire, and the Blood sugar test were used. The CFT group received 8 sessions of intervention (each for 2 h). Multivariate analysis of covariance was used to data analysis. Results: The results showed that CFT had a significant effect on alexithymia (F=9/27, p=0/006), adaptive behavior (F=6/75, p=0/016), and treatment adherence (F=15/26, p=0/001) of patients with type 2 diabetes in the post-test. There was no significant difference between the experimental and control groups in Blood sugartest (F=0/08, p=0/786). Conclusion: Based on the findings, it can be concluded that CFT is effective in increasing the level of adaptive behavior and adherence to treatment.
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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 | 0.000 | 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".