How and Why Telehealth Interventions Work for Improving Self-Care among Vulnerable Groups of Heart Failure Patients: A Rapid Realist Synthesis (Preprint)
Bibliographic record
Abstract
BACKGROUND Heart Failure (HF) is a prevalent condition among older adults in Canada, often leading to reduced quality of life and frequent hospitalizations. Heart Failure Disease Management Interventions (HFDMIs), particularly those delivered through telehealth, aim to improve care by fostering self-care and reducing readmissions. However, disparities in access and utilization of heart failure telehealth services persist within marginalized populations. OBJECTIVE The purpose of this paper is to present the findings of a scoping review and a rapid realist synthesis around HF telehealth interventions for vulnerable groups of patients with HF. This review is underpinned by the meta-theory of CR and intersectionality theory. METHODS A rapid realist synthesis was undertaken for the retrieved literature to explore the underlying mechanisms and contexts that make HF telehealth interventions work or not work for marginalized groups of HF patients. RESULTS The realist review findings indicate that vulnerable patients require simple interventions. The findings also suggest that for effective utilization of telehealth and remote monitoring services, these patients require simplified training that could increase their confidence. The review findings have also demonstrated that involving patients’ family members in the delivery of telehealth interventions ensures success. CONCLUSIONS Future research with vulnerable populations should be underpinned by the critical/ intersectionality theory and should apply the principles of intersectionality at all stages of the research process, including evaluation and analysis. This review also urges HF practitioners to apply the principles of intersectionality and health equity in clinical practice, such that the interventions are simple, personalized, involve family members, include an in-person component, include patients’ and health professionals’ training, and integrate telemonitoring data.
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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 | 0.084 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".