Better together: Relationship quality and mental health among cardiac patients and spouses
Bibliographic record
Abstract
Reductions in marital relationship quality are pervasive post-cardiac event. It is not yet understood how relationship quality is linked to mental health outcomes in couples where one member has established cardiovascular disease (CVD) and the interdependence within dyads is seldom measured. This research is required as psychological distress has been independently linked to CVD incidence, morbidity, and mortality. This study assessed associations of relationship quality with depression and anxiety among patients with CVD and their spouses. Participants completed questionnaires measuring four dimensions of relationship quality and mental health. Data were analyzed using an Actor-Partner Interdependence Model with hierarchical moderation analyses. 181 dyads (N = 362 participants) comprised the study sample. Most patients had coronary artery disease (66.3%) and 25.9% were female. Patients reported higher relationship satisfaction and fewer anxiety symptoms than did spouses. Patients and spouses with high dyadic consensus and affectional expression reported fewer mental health symptoms, but only when the other partner also perceived high levels of consensus and affectional expression in the relationship. Patients and spouses with low dyadic cohesion reported worse mental health symptoms (actor effects), but those effects were no longer significant when both the patient and the spouse appraised the relationship as having high levels of dyadic cohesion. Taken together, relationship quality is linked to mental health symptoms in patients with CVD and their spouses. Longitudinal and experimental studies are now warranted to further substantiate the cross-sectional findings of this study.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".