Cognition, mood, self-care, and medication adherence in hospitalized patients with heart failure: a cross-sectional study
Bibliographic record
Abstract
Poor medication adherence is prevalent in patients with heart failure (HF). Cognitive impairment (CI), mood (anxiety and depression), poor self-care are commonly seen and associated with medication adherence issues. However, it is not well established how these factors interact with each other impacting on HF patients' medication adherence. Therefore, the objective of this study was to determine relationships between cognition, mood, self-care and medication adherence of HF patients. We conducted a cross-sectional study and collected 200 hospitalized HF patients from Cardiology department of Linyi People's hospital in Shandong province, China. Cognition, mood, self-care and medication adherence were assessed by using Medication Adherence Self-Efficacy Scale-Revision (MASES-R), Montreal Cognitive Assessment (MoCA), Hospital Anxiety and Depression Scale (HADS) and Self-Care of Heart Failure Index version 7.2 (SCHFI v7.2), respectively. The results showed that the average score of MoCA, HADS, SCHFI, and MASES-R were 14.94 ± 8.13, 15.91 ± 12.46, 145.8 ± 67.5, 34.2 ± 14.0, respectively. Education, cognition, self-care were positively correlated with medication adherence. By comparison, sex, age, atrial fibrillation, NT-proBNP, duration of HF and mood (anxiety and depression) were negatively correlated with medication adherence. Mood and self-care had a direct impact on medication adherence. Cognition and self-care had an indirect impact on medication adherence through mood. Interventions should consider these interactions to improve medication adherence in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".