Social determinants of health and slippery slopes in assisted dying debates: lessons from Canada
Bibliographic record
Abstract
The question of whether problems with the social determinants of health that might impact decision-making justify denying eligibility for assisted dying has recently come to the fore in debates about the legalisation of assisted dying. For example, it was central to critiques of the 2021 amendments made to Canada's assisted dying law. The question of whether changes to a country's assisted dying legislation lead to descents down slippery slopes has also come to the fore-as it does any time a jurisdiction changes its laws. We explore these two questions through the lens of Canada's experience both to inform Canada's ongoing discussions and because other countries will confront the same questions if they contemplate changing their assisted dying law. Canada's Medical Assistance in Dying (MAiD) law has evolved through a journey from the courts to Parliament, back to the courts, and then back to Parliament. Along this journey the eligibility criteria, the procedural safeguards, and the monitoring regime have changed. In this article, we focus on the eligibility criteria. First, we explain the evolution of the law and what the eligibility criteria were at the various stops along the way. We then explore the ethical justifications for Canada's new criteria by looking at two elements of the often-corrosive debate. First, we ask whether problems with the social determinants of health that might impact decision-making justify denying eligibility for assisted dying of decisionally capable people with mental illnesses and people with disabilities as their sole underlying medical conditions. Second, we ask whether Canada's journey supports slippery slope arguments against permitting assisted dying.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.022 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".