Ethical issues in nursing home palliative care: a cross-national survey
Bibliographic record
Abstract
OBJECTIVES: With an increased dependency on nursing homes to provide care to the ageing population, it is likely that ethical issues will also increase. This study aimed to identify the type of ethical issues and level of associated distress experienced by nurses providing palliative care in nursing homes in the UK and Canada, and pilot the Ethical issues in Palliative Care for Nursing Homes (EPiCNH) instrument in Canada. METHODS: A cross-sectional survey design was used. One hundred and twenty-three nurses located in 21 nursing homes across the UK and Canada completed the EPiCNH instrument. RESULTS: Frequent ethical issues include upholding resident autonomy, managing family distress, lack of staff communication and lack of time in both countries. Higher levels of distress resulted from poor communication, insufficient training, lack of time and family disagreements. Nurses in Canada experienced a greater frequency of ethical issues (p=0.022); however, there was no statistical difference in reported distress levels (p=0.53). The survey was positively rated for ease of completion, relevance and comprehensiveness. CONCLUSIONS: Nurses' reported comparable experiences of providing palliative care in UK and Canadian nursing homes. These findings have implications on the practice of care in nursing homes, including how care is organised as well as capacity of staff to care for residents at the end of life. Training staff to take account of patient and family values during decision-making may address many ethical issues, in line with global policy recommendations. The EPiCNH instrument has demonstrated international relevance and applicability.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".