Predictors of Heart Failure Self-care in Patients Who Screened Positive for Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is associated with cognitive impairment, which could negatively affect a patient's abilities to carry out self-care, potentially resulting in higher hospital readmission rates. Factors associated with self-care in patients experiencing mild cognitive impairment (MCI) are not known. OBJECTIVE: This descriptive correlation study aimed to assess levels of HF self-care and knowledge and to determine the predictors of self-care in HF patients who screen positive for MCI. METHODS: The Montreal Cognitive Assessment was used to screen for MCI. In 125 patients with MCI hospitalized with HF, self-care (Self-care of Heart Failure Index) and HF knowledge (Dutch Heart Failure Knowledge Scale) were assessed. We used multiple regression analysis to test a model of variables hypothesized to predict self-care maintenance, management, and confidence. RESULTS: Mean (SD) HF knowledge scores (11.24 [1.84]) were above the level considered to be adequate (defined as >10). Mean (SD) scores for self-care maintenance (63.57 [19.12]), management (68.35 [20.24]), and confidence (64.99 [16.06]) were consistent with inadequate self-care (defined as scores <70). In multivariate analysis, HF knowledge, race, greater disease severity, and social support explained 22% of the variance in self-care maintenance (P < .001); age, education level, and greater disease severity explained 19% of the variance in self-care management (P < .001); and younger age and higher social support explained 20% of the variance in self-care confidence scores (P < .001). Blacks, on average, scored significantly lower in self-care maintenance (P = .03). CONCLUSION: In this sample, patients who screened positive for MCI, on average, had adequate HF knowledge yet inadequate self-care scores. These models show the influence of modifiable and nonmodifiable predictors for patients who screened positive for MCI across the domains of self-care. Health professionals should consider screening for MCI and identifying interventions that address HF knowledge and social support. Further research is needed to explain the racial differences in self-care.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".