Palliative Care for the Elderly With Heart Diseases in Tertiary Health care: A Concept Analysis
Bibliographic record
Abstract
BACKGROUND: The increasing incidence of heart failure (HF) in the elderly leads to increased mortality, hospitalization, length of hospital stay, and health care costs. Older adults often face multiple drug treatments, comorbidities, frailty, and cognitive problems, which require early palliative care. However, these patients do not receive adequate palliative care. OBJECTIVE: This concept analysis aimed to develop an in-depth understanding of palliative care for elderly patients with cardiac diseases in tertiary care. DESIGN: The analysis was guided by Walker and Avant's method, and databases were searched using keywords, such as palliative care, tertiary care, elderly, and heart. Covidence was used to review the results using the inclusion and exclusion criteria. RESULTS: The World Health Organisation's definition of palliative care is widely accepted. Palliative care for older adults with heart disease in tertiary care is preceded by chronic illness, polypharmacy, symptom burden, physical and cognitive decline, comorbidities, and psychosocial/spiritual issues. The main attributes of palliative care for this population include health care professionals and patient education, holistic patient/family-centered care, symptom management, shared decision-making, early integration, advanced care planning, and a multidisciplinary approach. Palliative care improves elderly cardiac patients' and their family satisfaction while reducing readmission, hospital stays, and unnecessary invasive procedures. CONCLUSION: Collaboration between hospitals, community organizations, transitional palliative care services, and research has the potential to improve early palliative care and the well-being of the elderly cardiac population. Advanced Practice Nurses (APNs) competencies play a crucial role in promoting palliative care in the elderly HF population.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".