Voice Your Values, a Tailored Advance Care Planning Intervention in Persons Living with Early Dementia: A Pilot Study
Bibliographic record
Abstract
A palliative approach to care aims to meet the needs of patients and caregivers throughout the trajectory of a chronic disease, such as dementia, and can be delivered by clinicians who are not specialists in palliative care. Advance care planning (ACP) is an important component of the palliative approach. Timely ACP can improve outcomes for persons living with dementia (PLwD) and their care partners. Yet only a minority of PLwD participate in any ACP discussions. There remains a lack of evidence-based guidelines to inform clinical practice, specifically related to ACP for those living with early dementia. To address this gap, an intervention called Voice Your Values (VYV) was developed, that healthcare professionals can implement to identify and document the values and wishes of PLwD for their future care. The tailored VYV intervention was evaluated using a single group pre-test and post-test design to determine its feasibility, acceptability, and preliminary efficacy. A convenience sample of 21 dyads of PLwD and their trusted individuals, such as family and friends, was recruited from five geriatric clinics in Ontario. The VYV intervention was delivered to dyads over two sessions using a secure videoconferencing platform. The recruitment rate was lower (52%) than expected (60%); however, the retention rate was high at 94%, and 100% of the participants rated VYV as highly acceptable. The PLwD demonstrated improvement in ACP engagement (p=.00). The trusted individuals showed improvement in decision-making confidence (p=.01) and psychological distress (p=.02), but no improvement in dementia knowledge (p=.22). The video and sound quality were rated highly. All PLwD were able to articulate and document their values and wishes related to terminal and vegetative states. The VYV study experienced some difficulties with recruitment. As well, some intervention sessions took longer than expected to deliver, highlighting the need for ACP conversations and the high level of engagement from participants, but also raising concern about the practicality of such lengthy conversations in actual clinical settings with limited resources. That said, the study demonstrated a high retention rate, intervention fidelity, and acceptability. Future studies are needed to further refine the VYV intervention and establish its efficacy using randomization and larger sample sizes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".