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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".