Collaborating with palliative care teams to provide end of life care for patients with heart failure: An integrative review
Bibliographic record
Abstract
Advanced heart failure (HF) can be difficult for nurse practitioners (NPs) to manage in primary care due to the unpredictable nature of the condition. Further, barriers that patients with HF experience in regards to receiving end of life (EOL) care hinder NP collaboration with palliative care teams. The goal of the project is to answer: How can NPs working in a primary care setting collaborate with palliative care teams to provide patients who have HF with EOL care? A literature search was conducted using the Cumulative Index of Nursing and Allied Health, PubMed Medline, PsychInfo, Social Work Abstracts and the National Guideline Clearinghouse electronic databases. Evidence was also gathered using backward and forward reference searching, and highly relevant grey literature from the BC Heart Failure Network. The Canadian Interprofessional Health Collaborative (CIHC) framework was used to describe the theoretical underpinnings of this paper by outlining the factors involved to achieve interprofessional collaboration. There were 33 articles retrieved during the literature search to inform how NPs can collaborate with palliative care teams for patients with HF at EOL. There were no articles that answered the research question directly. Instead, barriers and issues for patients with HF receiving EOL care were identified in the findings. Nurse practitioners can collaborate with palliative care teams by addressing the barriers to EOL care for patients with HF that relates to communication, leadership, role clarification, team functioning, and conflict resolution. Nurse practitioners should be encouraged to collaborate with palliative care teams to improve accessibility to palliative care for patients with HF at EOL. Future research is needed to directly inform how collaboration can occur with palliative teams to provide patients who have HF with EOL care.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".