Marital quality and loneliness as predictors for subjective health status in cardiac rehabilitation patients following percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: Low marital quality is associated with adverse health outcomes and lower personal well-being. Loneliness increases the risk of cardiovascular disease and mortality and predicts poor quality of life. The aim of this study was to investigate the association between marital quality and loneliness and subjective health status in primary percutaneous coronary intervention (pPCI) patients who underwent cardiac rehabilitation (CR). DESIGN/METHODS: In a prospective cohort study, pPCI patients that followed CR were included between 2009-2011. A total of 223 patients responded to the Short Form 12 (SF-12) (subjective health status), Maudsley Marital Questionnaire (MMQ-6) (marital quality) and University of California, Los Angeles - Revised (UCLA-R) questionnaires at baseline (pre-CR) and at three months (post-CR) or at 12 months follow-up. Subjective health status is displayed by a physical component summary (PCS) score and a mental component summary (MCS) score. Generalized estimating equation (GEE) analyses were performed to test improvements in subjective health status. RESULTS: Changes over time in subjective health status scores were similar between patients with optimal marital quality vs patients with less optimal marital quality and non-lonely patients vs lonely patients. The MCS level at one-year follow-up of both patients with less optimal marital quality and lonely patients was lower compared with a healthy Dutch population (respectively; mean MCS score 47.3 (standard deviation (SD) 10.5); p = 0.013 and mean MCS score 46.1 (SD 11.2); p = 0.010). CONCLUSION: Both patients with less optimal marital quality and lonely patients did not reach the MCS level of a healthy Dutch population. Therefore, extra care and support should be given to these patients in a CR programme.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".