Medical Assistance in Dying, Palliative Care, Safety, and Structural Vulnerability
Bibliographic record
Abstract
As more jurisdictions consider legalizing medical assistance in dying or assisted death (AD), there is an ongoing debate about whether AD is driven by socioeconomic deprivation or inadequate supportive services. Attention has shifted away from population studies that refute this narrative, and focused on individual cases reported in the media that would appear to support these concerns. In this editorial, the authors address these concerns using recent experience in Canada, and argue that even if we accept these stories at face value, the logical policy response would be to address the root causes of structural vulnerability rather than attempt to restrict access to AD. In terms of concerns about safety, the authors go on to point out the parallels between media reports about the misuse of AD and reports of wrongful deaths due to the misuse of palliative care (PC) in jurisdictions where AD was not legal. Ultimately, we cannot justify having a different response to these reports when they apply to AD instead of PC, and nobody has argued that PC should be criminalized in response to such reports. If we are skeptical of the oversight mechanisms used for AD in Canada, we must be equally skeptical of the oversight mechanisms used for end-of-life care in every jurisdiction where AD is not legal, and ask whether prohibiting AD protects the lives of the vulnerable any better than legalization of AD with safeguards.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".