Eligibility for assisted dying: not protection for vulnerable people, but protection for people when they are vulnerable
Bibliographic record
Abstract
Downie and Schuklenk1 provide a clear narrative of the development of Canadian policy on medically assisted dying. This is very helpful for considering specific aspects of the continuing deliberations in Canada. This commentary presents an alternative perspective on the authors’ argument that narrow eligibility criteria for medical assistance in dying (MAiD) are discriminatory and unjustified. I argue that disability or mental illness as sole reason for accessing MAiD removes protections for all people who have times in their life when they have mental illness or disability combined with intolerable suffering. I claim that individuals do not make their best, self-actualising decisions at those times and the supposed protections of vague terms such as incurable, advanced and decline are not clinically useful for evaluating eligibility for individuals with chronic health conditions. Within discussion of the impact of social determinants of health on access to MAiD, the authors present the argument that decisionally capable people with mental illness or disabilities as their sole underlying condition should be allowed to access MAiD. The authors state that opposing this access sacrifices patient rights and strips people of their agency. To support their argument, they embrace the idea that people with mental illness or disabilities belong to an identifiable cohort. They claim that …
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.045 | 0.044 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".