Nursing care plan for people with heart failure in palliative care
Bibliographic record
Abstract
Introduction: Heart failure (HF) is significant due to its prevalence, hospitalization, and morbidity and mortality rates, and the impact the disease has on the lives of people, their families and the health system. Although advances in treatment have improved clinical conditions, people with HF can progress to the most severe stages, requiring palliative care.Objective: To draw up a proposal for a nursing care plan based on the Basic Human Needs Theory for people with HF in palliative care.Method: Case study, with a person with HF, using the instruments: nursing consultation, Barthel Index, Edmonton Symptom Scale, Diagnosis, Results and Nursing Interventions, analyzed and classified based on the references of the Basic Human Needs Theory and Total Pain.Results: Based on clinical reasoning and the Outcome-Present State-Test model, eight diagnoses were prioritized to make up the nursing care plan.Discussion: Clinical assessment based on theoretical references, scientific methods and standardized nursing classifications contributes to the best evidence, the development of clinical reasoning and the development of effective nursing care plan for people with HF.Conclusion: Clinical, logical, critical, and analytical reasoning facilitated the development of a care plan aligned with the multidimensional demands presented by the patient.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".