Embracing the End: A Comparative Analysis of Medical Aid in Dying in Canada and the United States
Bibliographic record
Abstract
Since the late nineteenth century, debate has unfolded over the use of euthanasia and physician-assisted death to alleviate the suffering of individuals with medical illnesses. The controversy surrounding the issue persists and its implications are significant. While most countries prohibit Aid in Dying (AID), legalization of the practice has expanded globally in recent years. Canada and the United States (US) are two such jurisdictions that have expanded access to AID. Canada has federally legalized the practice, which the country refers to as Medical Aid in Dying (MAID), and in 2021, the country expanded the eligibility criteria for individuals seeking access MAID. Today, an individual in Canada is eligible for MAID even if a natural death is not “reasonably foreseeable.” In contrast, the US prohibits the practice on the federal level but allows states to legalize it as they wish. While the list of states allowing the practice has grown, currently, only eleven US jurisdictions allow some form of AID. This Note analyzes the current AID legal regime in the US and Canada and compares the different approaches that each country has taken. It then argues that the US should borrow elements from the Canadian model by expanding access to AID services on the federal level, allowing federal funding to be used for AID services in states that have legalized the practice, and standardizing reporting requirements.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".