Factors Associated With Hope and Quality of Life in Patients With Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Psychological resources such as hope have been suggested to affect quality of life (QoL) positively in patients with heart disease. However, little information regarding the relationship between these two constructs is available. PURPOSE: This work was aimed at examining the factors associated with hope and QoL in patients with coronary artery disease. METHODS: In this descriptive work, perceived QoL and hope were assessed in 500 patients with heart disease. The information was collected using the McGill QoL Questionnaire, demographic variables, and the Herth Hope Index. The Pearson correlation test and general linear model were used to examine correlations through SPSS Version 22. RESULTS: A considerable correlation was discovered between QoL and hope (r = .337, p < .001). Multivariate analyses with regression revealed that religious beliefs and social support both had significant and positive effects on the total perceived hope of patients and that patient age had a considerable negative impact on QoL (p < .05). None of these factors had a significant impact on hope (p < .05). In addition, the total QoL had a significant and positive effect on patient feelings and thoughts, whereas the physical problems component of QoL had a significant and negative effect on hope (p < .05). Participants with higher levels of education reported more hope. CONCLUSIONS: QoL relates significantly to self-perceived hope in patients. Understanding QoL and hopefulness in patients with coronary artery disease has implications for nurses and other healthcare professionals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".