Self-Efficacy and Health Status in Patients With Coronary Heart Disease: Findings From the Heart and Soul Study
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between cardiac self-efficacy and health status, including symptom burden, physical limitation, quality of life, and overall health among outpatients with stable coronary heart disease (CHD). We hypothesized that lower self-efficacy would predict worse health status, independent of CHD severity and depression. METHODS: We performed a cross-sectional study of 1024 outpatients with CHD, who were recruited between 2000 and 2002 for the Heart and Soul Study. We administered a validated measure of cardiac self-efficacy, assessed cardiac function using exercise treadmill testing with stress echocardiography, and measured depressive symptoms using the Patient Health Questionnaire. Health status outcomes (symptom burden, physical limitation, and quality of life) were assessed using the Seattle Angina Questionnaire, and overall health was measured as fair or poor (versus good, very good, or excellent). RESULTS: After adjustment for CHD severity and depressive symptoms, each standard deviation (4.5-point) decrease in self-efficacy score was independently associated with greater symptom burden (adjusted odds ratio (OR) = 2.1, p = .001), greater physical limitation (OR = 1.8, p < .0001), worse quality of life (OR = 1.6, p < .0001), and worse overall health (OR = 1.9, p < .0001). Depressive symptoms and poor treadmill exercise capacity were also associated with poor health status, but left ventricular ejection fraction and ischemia were not. CONCLUSIONS: Among patients with CHD, low cardiac self-efficacy is associated with poor health status, independent of CHD severity and depressive symptoms. Further study should examine if self-efficacy constitutes a useful target for cardiovascular disease management interventions.
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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 | 0.004 |
| 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 | 0.001 | 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".