Advance requests for MAiD in dementia: Policy recommendations emerging from a mixed-methods study of the views of the Canadian public and MAiD practitioners
Bibliographic record
Abstract
Abstract Background The Canadian public has repeatedly expressed its desire for advance requests for Medical Assistance in Dying (MAiD) after dementia diagnosis, yet MAiD practitioners’ willingness to accede to such advance requests is unknown. This study explores the extent and nature of any gap between the public’s desire for, and practitioners’ willingness to provide MAiD, and identifies policies to ameliorate such a gap. Methods Two complementary mixed-method surveys gathered data from convenience samples of 83 Canadian adults and 103 MAiD practitioners. The public survey asked participants which of five specific circumstances commonly encountered in dementia they would include in their advance requests. The practitioner survey queried the validation level participants would require before providing MAiD in each specific circumstance. Participants’ reasons were probed using thematic analysis of open-ended questions. Results On average, 77% of public participants indicated they definitely or probably would include each of the five specific circumstances in their advance requests for MAiD. As validation level decreased from patient consent to patient assent, family assent, or advance request alone, the magnitude of the gap between the public’s desire and practitioners’ willingness increased. The practitioners’ qualitative data contained many practical insights from which emerged seven policy recommendations to ameliorate this gap and increase the likelihood of honouring patient requests. Interpretation The study provides evidence of a gap between public desire for, and practitioner willingness to provide MAiD in dementia. The policy recommendations are relevant to consideration of legislation for advance requests for MAiD.
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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 | 0.083 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".