Interventions and Predictors of Transition to Hospice for People Living With Dementia: An Integrative Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Goal-concordant transition to hospice is an important facet of end-of-life care for people living with dementia. The objective of this integrative review was to appraise existing evidence and gaps focused on interventions and predictors of transition to hospice and end-of-life care for persons living with dementia across healthcare to inform future research. RESEARCH DESIGN AND METHODS: Using integrative review methodology by Whittemore and Knafl, 5 databases were searched (PubMed, CINAHL, Web of Science, Google Scholar, and Cochrane Database for Systematic Reviews) for articles between 2000 and 2023. The search focused on dementia, hospice care, transitions, care management and/or coordination, and intervention studies. RESULTS: Fourteen articles met inclusion criteria after critical appraisal. Most were cross-sectional in design and conducted in nursing homes and hospitals in the U.S. persons living with dementia had multiple chronic conditions including cancer, diabetes, heart disease, and stroke. Interventions included components of hospice decision-making delivered through advance care planning, checklist-based care management for hospice transition, and palliative care for those with severe dementia. Predictors included increasing severity of illness including functional decline, organ failure, intensive care use, and the receipt of palliative care. Other predictors were related to insurance status, race and ethnicity, and caregiver burden. Overall, despite moderate to high-quality evidence, the studies were limited in scope and sample and lacked racial and ethnic diversity. DISCUSSION AND IMPLICATIONS: Prospective, multisite randomized trials and population-based analyses including larger and diverse samples are needed for improved end-of-life dementia illness counseling and hospice care transitions for persons living with dementia and their caregivers.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".