Predictors of Health-Related Quality of Life among Thai People with Coronary Heart Disease: A Preliminary Study
Bibliographic record
Abstract
Objective: To examine the prediction of severity of illness, health promoting behaviors, cardiac self-efficacy, and acceptance of illness on health-related quality of life (HRQOL) among Thai people with coronary heart disease (CHD) in Thailand.Material and Methods: A cross-sectional design was used. A quota sampling was used to recruit 110 people with CHD, who met the inclusion criteria, from 20 selected hospitals across Thailand. Five self-reporting questionnaires were used: a demographic data questionnaire, Thai version of MacNew HRQOL, Thai version of Health-Promoting Lifestyle Profile-II, Thai version of Cardiac Self-Efficacy Scale Questionnaire, and the Thai version of Acceptance of Illness Scale. Data were analyzed using descriptive statistics, a point-biserial correlation or a bivariate Pearson’s correlation and multiple stepwise regression analyses. Results: One hundred and ten people were included. Most of the participants were men (64.5%) with an average age of 62.07±9.98 years. Most of them (76.4%) were best categorized as class I under the Canadian Cardiovascular Society (CCS) classification system. The findings showed that 46 percent of the variance (adjusted R2 =0.46) for HRQOL was explained by being CCS class I (β=0.22, p-value<0.010), cardiac self-efficacy (β=0.41, p-value<0.010) and acceptance of illness (β=0.35, p-value<0.010). Health promoting behavior was a non-significant predictor of HRQOL (β=0.10, p-value=0.260).Conclusion: The results support the severity of illness, cardiac self-efficacy, and acceptance of illness in explaining HRQOL among people with CHD. Therefore, nursing interventions that are suitable for the severity of the disease, and aimed at boosting cardiac self-efficacy and acceptance of illness should be considered to enhance HRQOL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".