Medical Assistance in Dying in a Profit-Driven Health System: Ethical and Equity Challenges in the U.S. vs. Canada
Bibliographic record
Abstract
Introduction Medical Assistance in Dying (MAID), legalized in Canada in 2016, now accounts for 4.1% of all deaths nationwide and 6.6% in Quebec as of 2022(1). In Canada’s single-payer system, MAID is integrated as a publicly funded medical service. In contrast, the U.S.’s fragmented, market-driven system is defined by profit maximization, inequitable access, and insurer- driven decision-making. While MAID aims to promote autonomy and compassion, introducing it into the American context raises profound ethical concerns, particularly regarding nonmaleficence and systemic coercion. This poster evaluates the unique risks MAID poses within the U.S. healthcare system, particularly for vulnerable populations.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".