A scoping review of research to assess the frequency, types, and reasons for end-of-life care setting transitions
Bibliographic record
Abstract
Aims: Most people approaching the end of life develop care needs, which typically change over time. Moves between care settings may be required as health deteriorates. However, in some cases, care setting transitions may have little to do with end-of-life care needs and instead reflect the needs, demands, availability, or funding provisions of the country or funding body and organizations providing care. This paper is a scoping review of the international peer-reviewed research literature to gain evidence on the frequency and types of end-of-life care setting transitions, and the reasons for these moves. Methods: All relevant print and open access research articles published in 2000+ were sought using the Directory of Open Access Journals and EBSCO Discovery Host. Results: A total of 39 research articles were identified and reviewed. However, minimal useful evidence was revealed. Most articles focused solely on hospital admissions near death, and some focused on nursing home admissions, with other moves infrequently studied. Conclusions: This review demonstrates the need to quantify and justify end-of-life care setting transitions as it appears dying people are frequently moved, often as death nears. This research is needed to distinguish transitions related to end-of-life care needs and those arising from pressures on or from care providers and others unrelated to the person’s care needs.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".