Resources to Support Decision-Making Regarding End-of-Life Nutrition Care in Long-Term Care: A Scoping Review
Bibliographic record
Abstract
Resources are needed to aid healthcare providers and families in making end-of-life nutrition care decisions for residents living in long-term care settings. This scoping review aimed to explore what is reported in the literature about resources to support decision-making at the end of life in long-term care. Four databases were searched for research published from 2003 to June 2023. Articles included peer-reviewed human studies published in the English language that reported resources to support decision-making about end-of-life nutrition in long-term care settings. In total, 15 articles were included. Thematic analysis of the articles generated five themes: conversations about care, evidence-based decision-making, a need for multidisciplinary perspectives, honouring residents' goals of care, and cultural considerations for adapting resources. Multidisciplinary care teams supporting residents and their families during the end of life can benefit from resources to support discussion and facilitate decision-making.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".