Providing End-of-Life Care in Care Homes for Older People
Bibliographic record
Abstract
The study aimed to explore the views of care home staff (CHS) and community nurses (CNs) on providing end-of-life care (EOLC) in care homes. Participants were randomly selected and qualitative interviews conducted with 80 CHS and 10 CNs. Themes emerging from the data included the following: The meaning of EOLC; starting EOLC; dying in the care home; stress of providing EOLC; improving EOLC; and the role of the CN. CHS felt that planning for the end of life was important before residents reached the dying phase, which some found difficult to determine. Although CHS wished to avoid residents being transferred to hospital to die, they acknowledged that improvements in their skills and the resources available to them were needed to manage EOLC effectively. CNs were critical of the EOLC provided in some care homes, reporting tensions over their relationship with CHS. As the number of older people who die in care homes increases, there is a need to overcome these barriers to provide good EOLC.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".