Development of a Structural Model of Quality of Life for Cardiac Patients Based on Health Locus of Control and Illness Perception with the Mediating Role of Alexithymia
Bibliographic record
Abstract
Objective: This study aimed to develop a structural model of the quality of life for cardiac patients based on health locus of control and illness perception with the mediating role of alexithymia in the cardiac patient population visiting hospitals in Tehran. Methods and Materials: The research method was correlational. A total of 281 patients were selected through purposive sampling and responded to the Minnesota Living with Heart Failure Questionnaire, the Illness Perception Questionnaire, the Multidimensional Health Locus of Control Scale, and the Toronto Alexithymia Scale. Data analysis was conducted using structural equation modeling. Findings: The results indicated that the structural model of quality of life for cardiac patients based on health locus of control and illness perception, mediated by alexithymia, was a good fit. Additionally, there was a significant positive relationship between health locus of control and illness perception with quality of life (p < 0.05), a significant negative relationship between alexithymia and quality of life (p < 0.05), and a significant negative relationship between health locus of control and illness perception with quality of life (p < 0.05). Conclusion: Attention to health locus of control and providing training to strengthen internal locus of control, designing psychological interventions based on illness perception, and training in emotional expression and management for psychosomatic patients like those with cardiac conditions are recommended.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".