Determinants of Self-care Behaviors in Community-Dwelling Patients With Heart Failure
Bibliographic record
Abstract
BACKGROUND AND RESEARCH OBJECTIVE: As the population ages, chronic conditions such as heart failure are becoming more prevalent. An important goal is to understand how patients with heart failure learn to manage the often debilitating disease symptoms. The research objective was to examine the determinants of general and therapeutic self-care behaviors among community-dwelling heart failure patients. Guided by Connelly's Model of Self-care in Chronic Illness, enabling and predisposing factors were evaluated using sociodemographic characteristics, functional ability, and psychological status. Self-care maintenance, self-efficacy, and self-care management characteristics were also evaluated. PARTICIPANTS AND METHODS: Using a cross-sectional design, a convenience sample of 65 ambulatory care patients were recruited. Data were collected through chart reviews and questionnaires. RESULTS AND CONCLUSIONS: Common self-care maintenance behaviors included taking medication as prescribed (95%), seeking physician guidance (80%), and following sodium dietary restrictions (70%). These behaviors were influenced by enabling characteristics such as psychological status (P = .030), ethnicity (P = .048), and comorbidity (P = .023). A unique finding was that self-care maintenance behaviors were significantly lower in aboriginal participants. The predisposing characteristic of self-efficacy influenced self-maintenance behaviors (P = .0002), overall self-care (P = .04) and number of hospital admissions (P < .0001). Higher overall self-care scores, measured by the summative Self-care Heart Failure Index score was correlated with fewer hospital admissions (P = .019).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".