Voice your values, a tailored advance care planning intervention in persons living with mild dementia: A pilot study
Bibliographic record
Abstract
Abstract Background In the early stage of dementia, persons living with dementia (PLwD) can identify their values and wishes for future care with a high degree of accuracy and reliability. However, there is a paucity of research to guide best practices on how best to incorporate advance care planning (ACP) in older adults diagnosed with mild dementia and therefore only a minority of these individuals participate in any ACP discussions. We developed an intervention called Voice Your Values (VYV) that healthcare professionals can implement to identify and document the values of PLwD and their trusted individuals such as friends or family. Purpose This single-group pre-test and post-test design aimed to determine the feasibility, acceptability, and preliminary efficacy of the VYV intervention. Methods A convenience sample of 21 dyads of PLwD and their trusted individuals were recruited from five outpatient geriatric clinics. The tailored VYV intervention was delivered to the dyads over two sessions using videoconferencing. Results In terms of feasibility, the recruitment rate was lower (52%) than the expected 60%; the retention rate was high at 94%, and the intervention fidelity was high based on the audit of 20% of the sessions. In terms of preliminary efficacy, PLwD demonstrated improvement in ACP engagement (p = <0.01); trusted individuals showed improvements in decision-making confidence (p = 0.01) and psychological distress (p = 0.02); whereas a minimal change was noted in their dementia knowledge (p = 0.22). Conclusion Most of the feasibility parameters were met. A larger sample along with a control group, as well as a longitudinal study, are requisite to rigorously evaluate the efficacy of the promising VYV intervention. There is emerging evidence that people living with mild dementia can effectively participate in identifying and expressing their values and wishes for future care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".