Advance Requests for Medical Assistance in Dying in Dementia: a Survey Study of Dementia Care Specialists
Bibliographic record
Abstract
BACKGROUND: Current Canadian Medical Assistance in Dying (MAiD) legislation requires individuals to have the mental capacity to consent at the time of the procedure. Advance requests for MAiD (ARs for MAiD) could allow individuals to document conditions where MAiD would be desired in the setting of progressive dementia. METHODS: Greater Vancouver area dementia care clinicians from family practice, geriatric medicine, geriatric psychiatry, and palliative care were approached to participate in an online survey to assess attitudes around the appropriateness of ARs for MAiD. Quantitative analysis of survey questions and qualitative analysis of open-ended response questions were performed. RESULTS: Of 630 clinicians approached, 80 were included in the data analysis. 64% of respondents supported legislation allowing ARs for MAiD in dementia. 96% of respondents articulated barriers and concerns, including determination of capacity, protecting the interests of the future individual, navigating conflict among stakeholders, and identifying coercion. 78% of respondents agreed with a mandatory capacity assessment to create an AR, and 59% agreed that consensus between clinicians and substitute decision-makers was required to enact an AR. CONCLUSION: The majority of Vancouver dementia care clinicians participating in this study support legislation allowing ARs for MAiD in dementia, while also articulating ethical and logistical concerns with its application.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".