An integrative literature review on nursing interventions aimed at increasing self-care among heart failure patients
Bibliographic record
Abstract
OBJECTIVE: to analyze and summarize knowledge concerning critical components of interventions that have been proposed and implemented by nurses with the aim of optimizing self-care by heart failure patients. METHODS: PubMed and CINAHL were the electronic databases used to search full peer-reviewed papers, presenting descriptions of nursing interventions directed to patients or to patients and their families and designed to optimize self-care. Forty-two studies were included in the final sample (n=4,799 patients). RESULTS: this review pointed to a variety and complexity of nursing interventions. As self-care encompasses several behaviors, interventions targeted an average of 3.6 behaviors. Educational/counselling activities were combined or not with cognitive behavioral strategies, but only about half of the studies used a theoretical background to guide interventions. Clinical assessment and management were frequently associated with self-care interventions, which varied in number of sessions (1 to 30); length of follow-up (2 weeks to 12 months) and endpoints. CONCLUSIONS: these findings may be useful to inform nurses about further research in self-care interventions in order to propose the comparison of different modalities of intervention, the use of theoretical background and the establishment of endpoints to evaluate their effectiveness.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".