Quality of Life in Coronary Heart Disease Patients: The Role of Defense Mechanisms and Alexithymia with Mediation of Psychological Distress
Bibliographic record
Abstract
Background: Coronary heart disease leads to negative consequences, diminished health-related quality of life, and increased mortality. Objectives: This study aimed at predicting the quality of life of patients with coronary heart disease based on defense mechanisms and alexithymia with the mediating role of psychological distress. Methods: In a cross-sectional descriptive study, 300 patients with coronary heart disease, who had referred to the specialized heart center of Ayatollah Madani governmental hospital in Khorramabad city of Lorestan were selected by the convenience sampling method from February to July 2015. Depression, anxiety, and stress scales, the mac-new health-related quality of life questionnaire for patients with heart disease, the Toronto alexithymia scale, the defensive styles scale and a demographical checklist were used for data collection. Descriptive indices were analyzed by SPSS-19 and structural equation model with AMOS was used for analysis of the inferential statistics. Results: According to the structural equation model, the path coefficient related to the effect of defense mechanisms on alexithymia (β = 0.65), alexithymia on the quality of life (β = -0.26), alexithymia on psychological distress (β = 0.51), and psychological distress on the quality of life (β = -0.58) were statistically significant (P < 0.001). However, the path of the defense mechanisms to psychological distress and the path of defense mechanisms to quality of life were not significant (P > 0.05). Conclusions: Defense mechanisms and alexithymia had a significant role in predicting the quality of life of patients with coronary heart disease. It is recommended that clinical specialists should design appropriate clinical trials or modify the current and future interventions on the basis of the results of such studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".