The Effectiveness of Mindfulness Based Stress Reduction Intervention on Emotion Regulation Problems and Blood Sugar Control in Patients With Diabetes Type II
Bibliographic record
Abstract
AIM & BACKGROUND: Diabetes is one of the most prevalent and costly chronic diseases that imposes many limitations on the activities of the patient. Stress reduction treatment based on mindfulness is an intervention which is used in mind-body medicine in order to reduce mental and physical disorders in patients with chronic diseases. So, this study aims to investigate the effectiveness of stress reduction treatment based on mindfulness on emotion regulation problems and glycemic control in patients with type 2 diabetes.METHODS & MATERIALS: The paper is an experimental study based on control and treatment groups with pre-test and post-test. 34 male and female patients with type 2 diabetes having at least high education, from Molasadra Clinic of Isfahan, were selected and were placed randomly in two groups of control (N=17) and treatment(N=17). Pre-test stage was done for both two groups by cognitive emotion regulation questionnaire (CERQ) and also by means of Glucometer to measure glycemic of patients. The treatment group for 8 sessions of 2 hours (once a week) was placed under the training of mindfulness-based stress reduction intervention. Afterwards, the post-test was done for both groups. The obtained data using SPSS software version 20 and multivariate analysis of covariance were analyzed.FINDINGS: The findings showed that MBSR had effect on emotion regulation problems, and glycemic control of patients with type 2 diabetes.CONCLUSIONS: On the basis of results, MBSR can have positive impact on emotion regulation problems, and glycemic control of patients.
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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 | 0.000 | 0.001 |
| 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".