Slowing the Slide Down the Slippery Slope of Medical Assistance in Dying: Mutual Learnings for Canada and the US
Bibliographic record
Abstract
Canada and California each introduced legislation to permit medical assistance in dying in June, 2016. Each jurisdiction publishes annual reports on the number of deaths that occurred under their respective legislations in the previous years. The numbers are disturbingly different. In 2021, 486 individuals died under California's End of Life Option. In the same year 10,064 Canadians died under that country's Medical Assistance in Dying (MAiD) legislation. California has a slightly larger population than Canada, and while medically assisted deaths as a percentage of total deaths remained virtually unchanged in California from 2020-2021, Canada saw a 30% increase from 2020 to 2021. This essay examines some of the factors propelling Canada down the slippery slope of medically assisted suicide, as well as those that may be keeping California and other US jurisdictions from taking the slide. At a time of increasing pressure in many jurisdictions (both nationally and internationally) to liberalize access to medical assistance in dying, some lessons from this comparative analysis are offered.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.018 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| 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".