What Ontario MAID Death Review Committee Reports Tell Us About Canada’s MAID Policy and Practice — And About the Overhaul It Needs
Bibliographic record
Abstract
This paper critically examines the evidence of Medical Assistance in Dying (MAID) practice in Ontario, as documented in reports from the Ontario Chief Coroner’s MAID Death Review Committee (MDRC), of which the author is a member. Drawing on case narratives and anonymized discussions summarized in a recent MDRC report on dementia, and in earlier MDRC reports, the author highlights troubling components of current MAID practice, particularly focusing on MAID of persons with dementia. The paper documents at times minimalistic capacity evaluations, questionable informed consent procedures, and flexible interpretations of legal criteria such as “reasonably foreseeable natural death” and “advanced state of irreversible decline.” The analysis reveals how current practices may circumvent criminal law-based legislative safeguards, including through the use of Waivers of Final Consent that resemble advance requests for MAID, which are prohibited under the Criminal Code. The paper argues that guidance documents of the Canadian Association of MAID Assessors and Providers contribute to practices that appear in tension with the law. In conclusion, the paper calls for an overhaul of the system, including through stricter legislative criteria, independent review mechanisms, and enhanced professional oversight, which should reflect the irreversible and most serious outcome of the procedure.
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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 | 0.044 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| 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".