Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the link between couple relationships and cardiovascular health, the plausible mechanisms by which relationship quality affects heart health, and to provide an overview of couple-based interventions aimed at improving cardiovascular health. RECENT FINDINGS: Marriage and the quality of the couple relationship bond are robust predictors of long-term health outcomes. Chronic relationship conflict and disconnection can be deadly. There are direct and indirect pathways by which couple relationship quality affect cardiovascular health. Direct pathways include effects of relationship quality on cardiovascular, neuroendocrine, and immune functioning. Indirect pathways include effects of emotional, cognitive and behavioural factors that impact lifestyle choices and adherence to treatment regimens. Effects of couple-based interventions addressing traditional cardiovascular risk factors have been null to modest and there is only one couple-based intervention that addressed relationship quality and heart health. On the basis of the literature, this is major oversight. We propose attachment-based interventions, such as our Healing Hearts Together program, for patients with heart disease and their partners. SUMMARY: Previous research indicates that couple-based interventions are promising. Large randomized controlled trials that aim to improve relationship quality among patients with CVD and their partners, as well as study mechanistic, surrogate, and clinical outcomes, are required to appropriately assess their impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".