Commentary: Ethics and Advance Requests for MAiD: Thresholds and Applicability
Bibliographic record
Abstract
We seek to highlight key ethical considerations that arise as Canada considers an expansion of medical assistance in dying (MAiD) to include advance requests. To do so, we will first highlight the ethical and practical concerns that arise with advance care planning and advance directives in general and then draw attention to the unique considerations that arise with advance requests for MAiD. Finally, we will take a closer look at the concerns that will arise with an expansion to include the population with dementia. We will argue that the stakeholder concerns for a vulnerable population such as dementia patients are significant. Legislative frameworks will need to address these concerns to ensure the safety of individual patients and support the role of surrogates and healthcare providers in this process.
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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 | 0.034 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.077 | 0.078 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".