Nursing care to patients who have the home as the preferred place of death: a scoping review
Bibliographic record
Abstract
BACKGROUND: The existing literature on nursing care for patients who choose home as their preferred place of death is scattered and lacks a coherent overview. This scoping review aimed to explore and categorize the available evidence on how nurses provide care for patients preferring to die at home. METHODS: Studies that included nurses and were focused on nursing care for patients who choose the home as their preferred place of death were included in the review. The scoping review considered studies with quantitative, qualitative, or mixed method designs; systematic reviews; and meta-analyses. No time restrictions were added. Key information sources were Medline, CINAHL (EBSCO), Scopus (Elsevier) and Google Scholar. Systematic reviews were searched for in the Cochrane Database of Systematic Reviews. Unpublished studies and grey literature were searched for in ProQuest Dissertations and Theses. The reference list of the studies included was searched. RESULTS: A total of 13 studies were deemed eligible for inclusion in the review, of which (n = 11) were qualitative and (n = 2) were both qualitative and quantitative. The studies were published between 2008 and 2023 and were conducted in the United Kingdom (n = 5), Norway (n = 4), Australia, Sweden, Canada and Japan. The studies included in this review highlighted issues of competence, resource limitations, flexibility as a coping mechanism, as well as collaboration and family caregivers. CONCLUSIONS: This review identified significant challenges in delivering nursing care for patients who prefer to die at home, including staff shortages, resource limitations, and educational deficiencies. Despite these barriers, nurses showed a strong commitment to patient care, highlighting the need for increased support and collaboration with family caregivers to improve home-based end-of-life care. IMPLICATIONS FOR RESEARCH: To improve care for patients who wish to die at home, it is crucial to address staff shortages and enhance nurse training to close knowledge gaps and ensure consistent, high-quality care. Healthcare systems must also allocate adequate resources to ensure that nurses have the necessary tools to deliver safe and effective care in home settings. Strengthening interdisciplinary collaboration will further enhance patient outcomes by supporting both nurses and family caregivers in end-of-life 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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".