A systematic review of the main mechanisms of heart failure disease management interventions
Bibliographic record
Abstract
OBJECTIVE: To identify the main mechanisms of heart failure (HF) disease management programmes based in hospitals, homes or the community. METHODS: Systematic review of qualitative and quantitative studies using realist synthesis. The search strategy incorporated general and specific terms relevant to the research question: HF, self-care and programmes/interventions for HF patients. To be included, papers had to be published in English after 1995 (due to changes in HF care over recent years) to May 2014 and contain specific data related to mechanisms of effect of HF programmes. 10 databases were searched; grey literature was located via Proquest Dissertations and Theses, Google and publications from organisations focused on HF or self-care. RESULTS: 33 studies (n=3355 participants, mean age: 65 years, 35% women) were identified (18 randomised controlled trials, three mixed methods studies, six pre-test post-test studies and six qualitative studies). The main mechanisms identified in the studies were associated with increased patient understanding of HF and its links to self-care, greater involvement of other people in this self-care, increased psychosocial well-being and support from health professionals to use technology. CONCLUSION: Future HF disease management programmes should seek to harness the main mechanisms through which programmes actually work to improve HF self-care and outcomes, rather than simply replicating components from other programmes. The most promising mechanisms to harness are associated with increased patient understanding and self-efficacy, involvement of other caregivers and health professionals and improving psychosocial well-being and technology use.
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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 | 0.034 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".