Study of the Efficacy of Cognitive Behavioral Group Treatment on Anger Rumination and Resilience of Cardiovascular Patients
Bibliographic record
Abstract
INTRODUCTION & PURPOSE: The purpose of the present study is to analyze the efficacy of cognitive behavioral group treatment on reducing anger rumination and increasing the resilience of cardiovascular patients.METHODOLOGY: The present study is quasi-experimental and follows a two- group pretest-posttest design. The statistical universe of the present study consists of all cardiovascular patients attending Tehran specialized treatment centers in 2015 for treatment of cardiovascular diseases. In the present study, 40 participants were selected from the research population and they were randomly assigned to two experimental and control groups (20 for experimental group and 20 for control group. For collecting data Sukhodolsky’s anger rumination scale questionnaire and Conner, K. M., & Davidson’s resilience scale questionnaire were implemented and for analyzing and examining the data multivariate covariance analysis test and single-variant covariance analysis have been employed.RESULTS: The research findings showed that cognitive behavioral group treatment leads to the reduction of mental rumination and increase of resilience among the group under the study and these results are statistically significant at 0.01 (P>0.01).CONCLUSION: According to the research finding it can be concluded that cognitive behavioral group treatment has a significant impact on this group and this treatment can be employed as an opposite solution to reduce the symptoms of those suffering from cardiovascular diseases and also to prevent the occurrence of such diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".