Surrogates’ end‐of‐life decision‐making process in nursing homes for residents with a neurocognitive disorder: An integrative review
Bibliographic record
Abstract
OBJECTIVE: The goal of this review is to analyse articles on the experience of surrogates who find themselves making end-of-life decisions for a relative with a major neurocognitive disorder in a nursing home. DESIGN: An integrative review of the literature based on Whittemore and Knafl's method. DATA SOURCES: This review used the CINAHL, PubMed, PsycInfo, Embase and Web of Science databases. A complementary search was also conducted via citation pearl searching, and the reference lists from the selected articles were manually verified. REVIEW METHOD: The quality of the selected articles was assessed using the Crow Critical Appraisal Tool, and the data were extracted systematically and were then organised according to Mishel's uncertainty in illness theory. The data that did not correspond to any concept of the theory were excluded at this stage. Analysis was conducted using the method put forward by Miles, Huberman and Saldaña. RESULTS: A total of 18 articles were selected: 11 qualitative, 5 quantitative and 1 using a mixed method, as well as 1 ethical argument. The subjects arising from the analysis of the articles were the types of decisions made, the support available for the surrogates, the role and involvement of the surrogates in the process and the factors that influence the decisions. CONCLUSION: The results of this integrative review stimulate reflection on the needs of family members involved in making decisions, as well as on the nursing practice and research. Published literature is mainly from North America, and thus, more research is needed to better understand the impact of cultural and ethnic differences in the process, which was poorly covered by the existing literature. Also, exploring nurses' involvement in supporting surrogates may eventually better equip nurses for their interventions with surrogates. IMPLICATIONS FOR PRACTICE: Describing the illness progression and the signification of palliative care to the resident with a NCD and their surrogate decision makers, as well as discussing end-of-life care preferences as early as possible are all nursing interventions that could potentially enhance surrogates' end-of-life decision-making process.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".