Motivational Interviewing and Self-care Practices in Adult Patients With Heart Failure
Bibliographic record
Abstract
BACKGROUND: Heart failure contributes to frequent hospitalizations, large healthcare costs, and high mortality. Heart failure management includes patient adherence to strict self-care practices (ie, symptom recognition, limiting sodium and fluids, monitoring weight, maintaining an active lifestyle, and medication adherence as well as monitoring other medical conditions). These practices can be difficult to enact and maintain. Motivational interviewing, although not studied extensively in patients with heart failure, may enhance patients' abilities to enact and maintain self-care practices. OBJECTIVE: The aim of this study was to examine the effectiveness of motivational interviewing on self-care practices in the adult population with heart failure. METHODS: We conducted a narrative systematic review of peer-reviewed research literature focused on motivational interviewing in adult patients with heart failure. The following databases were searched from database inception to March 2019: MEDLINE, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, ERIC, Educational Resource Complete, and Scopus. Of 1158 citations retrieved, 7 studies met the inclusion criteria. RESULTS: Outcomes were focused on self-care adherence (ie, maintenance, management, confidence), physical activity/exercise, and knowledge of self-care. Motivational interviewing has been effectively used either alone or in combination with other therapies and has been used in-home, over the telephone, and in hospital/clinic settings, although face-to-face interventions seem to be more effective. A number of limitations were noted in the included studies. CONCLUSION: Motivational interviewing is a potentially effective adjunct to enhance self-care practices in patients with heart failure. Further high-quality research is needed to support changes in clinical practice.
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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".