The nexus between cognitive function and self-care ablility in patients with chronic heart failure
Bibliographic record
Abstract
Background: The prevalence of chronic heart failure (CHF) within in the Western World remains high and is rapidly increasing in developing nations. The clinical trajectory of CHF is often characterised by chronic symptoms with periods of acute decompensation requiring hospitalisations for treatment. Yet almost half of these readmissions are potentially preventable through better adherence with self-care practices. For patients with CHF, self-care involves a level of confidence to adhere to behaviours that maintain physiological stability as well as recognising and making pertinent decisions in response to symptom changes. These self-care skills are not readily learnt and there are many barriers that account for this. Cognitive impairment occurs frequently in adults with CHF and is hypothesised to be a barrier in the acquisition of self-care skills. Aims: The research program is a series of systematically designed studies the specific aims were to: Review the literature surrounding the management of heart failure and teaching patients self-care behaviours; Identify clinical tools that measure heart failure self-care; Identify factors that appear to hinder the practice of self-care; Develop (Phase 1) and test (Phase 2) a conceptual model of factors that predict CHF self-care; compare the Mini Mental State Exam (MMSE) with the Montreal Cognitive Assessment (MoCA) in screening for Mild Cognitive Impairment (MCI); Examine the influence experience with heart failure symptoms has on self-care behaviours. Methods: The program consisted of comprehensive reviews of the literature, identification of clinical tools to measure self-care and cognitive function and two studies using descriptive survey methodology to develop (Phase 1) and test (Phase 2) a conceptual model of variables deemed to predict self-care.;Patients were recruited for the studies during their index hospital admission patients with CHF were assessed for self-care (Self-Care of Heart Failure Index) and screened for MCI and depressive symptoms (scores Scale). In Phase 1, patients were coded as MCI by scores 2, patients were coded as MCI by scores /or scores (MoCA). Adequate self-care was indicated by scores >70 on self-care domains: maintenance, management and confidence of the SCHFI. These factors along with demographic and clinical characteristics (age, gender, social isolation, education level, new diagnosis and co-morbid illnesses) were tested in multiple regression models for self-care. Results: In Phase 1 (n=50), seven variables (cognitive function, depressive symptoms, age, gender, social isolation, self-care confidence and co-morbid illnesses) explained 39% (F (7, 42) 3.80, p=0.003) of the variance in self-care maintenance and 38% (F (7,42) 3.73 p=0.003) of the variance in self-care management. Two variables were significant in predicting self-care maintenance: Age (p2 months. Conclusion: This systematically designed research program enhances the knowledge of heart failure self-care and provides a unique contribution in understanding the relationship between this phenomena and cognitive function. Cognitive impairment is a hidden co-morbidity in patients with CHF and to reflect this, the research program has henceforth been named the 'The InCOGNITO Heart Failure Study'. This co-morbidity impacts negatively upon self-care actions and decisions, potentially increasing the risk of hospital readmissions or even premature death. Such evidence suggests that CHF patients with MCI require ongoing support in developing self-care skills and behaviours directed at reducing CHF symptoms and improving their health and quality of life.;The research program provides further insight into the patient's experience of coping and adjusting to living with CHF. A number of other factors also help to predict self-care behaviours including the presence of symptoms causing functional limitations (NYHA class III or IV), higher co-morbidity, younger age and absence of depressive symptoms. These factors are a reflection of the complex nature of this syndrome and highlight the need for an integrated multidisciplinary health team in the long-term management of CHF. In order to clearly identify and articulate effective support and educational strategies directed at patients with CHF, ongoing research is required to further explore factors that hinder the application of teaching and counselling strategies and develop programs of care to specifically address these barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".