The Effectiveness of Emotion-Focused Therapy on Emotional Regulation, Quality of Life, and Pain Perception in Type 2 Diabetes Patients
Bibliographic record
Abstract
Objective: Given the increasing prevalence of diabetes, researchers in the field of health and well-being have been emphasizing empirical investigations. The aim of the current research was to examine the effectiveness of emotion-focused therapy on emotion regulation, quality of life, and pain perception in patients with type 2 diabetes. Methods and Materials: This semi-experimental study employed a pretest-posttest design with a control group. The study population consisted of type 2 diabetes patients affiliated with the International Diabetes Prevention and Control Foundation in Mashhad in June 2022. The sample included 30 individuals who were selected by simple random sampling and randomly assigned to two groups: 15 in the intervention group and 15 in the control group through random allocation. The intervention group received Greenberg and Goldman (2019) emotion-focused therapy over eight sessions, each lasting 90 minutes, once a week. Data were collected using utilizing the Emotion Control Questionnaire (1997), the World Health Organization Quality of Life questionnaire, and the McGill Pain Perception Questionnaire (1997) and analyzed using multivariate analysis of covariance in SPSS version 26. Findings: The results indicated that EFT improved emotion control (P=0.000, F=32.669), quality of life (P=0.000, F=20.360), and pain perception (P=0.000, F=94.358). Conclusion: it can be concluded that emotion-focused therapy leads to increased emotion control, improved quality of life, and reduced pain perception in patients 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) | 0.000 | 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) | 0.002 | 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".