Developing Country-Specific Question Prompt Lists About Dementia Palliative Care for Family Caregivers
Bibliographic record
Abstract
Abstract We aimed to develop question prompt lists for family caregivers of nursing home residents with advanced dementia to augment advance care planning conversations. In the context of a joint European-Canadian study, we used standardized nominal group methods to create country-specific lists of questions. (Bereaved) family caregivers of persons with dementia read an information booklet about end-of-life care for people with dementia, and generated questions to ask healthcare professionals. They also marked the most important questions from pre-selected questions from other lists. In the Netherlands, 20 participants contributed to a question prompt list of 24 questions that gravitated towards questions about terminating life and the responsibilities of physicians and family involved in decision making. In Canada, 4 participants came up with a question prompt list of 15 questions, related mostly to staff-family communication, with some the same as selected in the Netherlands. Data from the other countries will be presented too.
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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 | 0.057 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".