Healing Hearts Together — an emotionally focused intervention for couples after a cardiac event: a randomized controlled trial protocol
Bibliographic record
Abstract
Introduction: Couple relationships are important for health. Relationship distress is associated with increased incident and prognostic cardiovascular risk, while positive support is linked to heart-healthy behaviors and improved outcomes. This paper describes the study rationale, objectives, design, and methods of the Healing Hearts Together (HHT) randomized controlled trial (RCT). Objectives: The primary objective is to examine the difference in relationship quality between the 8-week HHT intervention group and usual care (UC) at program completion. Secondary objectives include evaluating the impact of HHT on relationship quality at 6 months, and mental health, quality of life, and cardiovascular risk factors measured at 8 weeks and 6 months post-intervention, as compared to usual care. Methods: Patients and their partners are recruited within 6 months of a cardiac event, procedure, or hospitalization and randomized 1:1 to HHT or UC. Assessments occur at baseline, 8 weeks, and 6 months follow-up. Analyses are planned as intention-to-treat, with multi-level analyses of covariance (ANCOVA) for the primary outcome: 8-week relationship quality as measured by the Dyadic Adjustment Scale. Secondary objectives will be evaluated using multi-level modeling for repeated measures. Anticipated results: It is expected that participants randomized to HHT will report higher relationship quality and improved secondary outcomes than will participants in UC. Conclusion: As the first study to evaluate a relationship-enhancement program for couples with cardiac disease, findings will have important clinical implications regarding the effect of relationship interventions on heart health.
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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.082 | 0.011 |
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".