Voice Your Values, Tailored Advance Care Planning in Persons Living With Mild Dementia: A Feasibility Study
Bibliographic record
Abstract
Abstract Older adults diagnosed with mild dementia can identify their wishes, values and goals of care with a high degree of accuracy and reliability. However, there is a paucity of research to guide best practices on how to incorporate Advance Care Planning (ACP) in the care of older adults living with mild dementia. Thus, only a minority of them participate in any ACP discussions. We developed an intervention called Voice Your Values (VYV) that healthcare professionals can implement to identify and document values of older adults. This single group pretest and posttest design aimed to determine the feasibility, acceptability and preliminary efficacy of the intervention. A convenience sample of 20 dyads of older adults and their trusted individuals were recruited from 4 geriatric clinics. Tailored VYV intervention was delivered to dyads on a one-on-one basis over two sessions using videoconferencing. Feasibility was determined through recruitment and retention rates, and intervention fidelity. Acceptability was assessed using modified Treatment Evaluation Inventory. Primary outcome was the Surrogate Decision-Making Confidence Scale. Secondary outcomes included an ACP engagement survey to assess older adults’ engagement in ACP; Dementia Knowledge Assessment Tool for trusted individuals; and the Kessler Psychological Distress Scale for all participants. The recruitment rate was 45%, retention rate was 100% and 92% participants rated VYV as highly acceptable. Trusted individuals showed statistically significant improvement in decision-making confidence (p=.02) and psychological distress (p=.02); but no improvement in dementia knowledge (p=.47). Older adults demonstrated statistically significant improvement in ACP engagement (p=<.01). Initial feasibility of VYV was demonstrated.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".