Developing a question prompt list for family caregivers concerning the progression and palliative care needs of nursing home residents living with dementia
Bibliographic record
Abstract
Objective: Communication around a palliative approach to dementia care often is problematic or occurs infrequently in nursing homes (NH). Question prompt lists (QPLs), are evidence-based lists designed to improve communication by facilitating discussions within a specific population. This study aimed to develop a QPL concerning the progression and palliative care needs of residents living with dementia. Methods: A mixed-methods design in 2 phases. In phase 1, potential questions for inclusion in the QPL were identified using interviews with NH care providers, palliative care clinicians and family caregivers. An international group of experts reviewed the QPL. In phase 2, NH care providers and family caregivers reviewed the QPL assessing the clarity, sensitivity, importance, and relevance of each item. Results: From 127 initial questions, 30 questions were included in the first draft of the QPL. After review by experts, including family caregivers, the QPL was finalized with 38 questions covering eight content areas. Conclusion: Our study has developed a QPL for persons living with dementia in NHs and their caregivers to initiate conversations to clarify questions they may have regarding the progression of dementia, end of life care, and the NH environment. Further work is needed to evaluate its effectiveness and determine optimal use in clinical practice. Innovation: This unique QPL is anticipated to facilitate discussions around dementia care, including self-care for family caregivers.
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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 | 0.089 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".