Views of Patients With Heart Failure on Their Value-Based Self-care Decisions
Bibliographic record
Abstract
BACKGROUND: Self-care adherence remains low in patients with heart failure (HF); little is known about the influence of patients' values on self-care decisions and behaviors. OBJECTIVES: The aim of this study was to explore how participants living with HF perceive their values and how those values are reportedly expressed in self-care decision making. METHODS: The Interpretative Phenomenological Analysis approach was used. Semistructured interviews were conducted with 12 patients 60 years or older; with New York Heart Association class II and III HF; and able to speak, read, and understand English. Participants recruited via convenience sampling (January-December 2016) from 2 urban sites in Western Canada. RESULTS: Values were reported to pivotally influence HF self-care decisions and behaviors. Overarching themes addressed aspects of values and decision making: notably, directness and complexity. Two main types of values, functional and emotional values, were involved in both adherent and nonadherent decisions. Values were often in flux, with the pursuit of these values being frequently in conflict with physical ability and changing over time. CONCLUSION: Two types of values serve influence self-care decisions and adherence. As HF and its self-care prevent patients from pursuing their prioritized values, patients are often nonadherent. Thus, patients with HF should be supported to find alternative ways to enact their values.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".