Developing country-specific questions about end-of-life care for nursing home residents with advanced dementia using the nominal group technique with family caregivers
Bibliographic record
Abstract
OBJECTIVE: We aimed to develop question prompt lists (QPLs) for family caregivers of nursing home residents with advanced dementia in the context of a study involving Canada, the Czech Republic, Italy, the Netherlands, the United Kingdom and Ireland, and to explore cross-national differences. QPLs can encourage family caregivers to ask questions about their relative's end-of-life care. METHODS: We used nominal group methods to create country-specific QPLs. Family caregivers read an information booklet about end-of-life care for people with dementia, and generated questions to ask healthcare professionals. They also selected questions from a shortlist. We analyzed and compared the QPLs using content analysis. RESULTS: Four to 20 family caregivers per country were involved. QPLs ranged from 15 to 24 questions. A quarter (24%) of the questions appeared in more than one country's QPL. One question was included in all QPLs: "Can you tell me more about palliative care in dementia?". CONCLUSION: Family caregivers have many questions about dementia palliative care, but the local context may influence which questions specifically. Local end-user input is thus important to customize QPLs. PRACTICE IMPLICATIONS: Prompts for family caregivers should attend to the unique information preferences among different countries. Further research is needed to evaluate the QPLs' use.
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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 | 0.052 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".