Regulating Death: A Brief History of Medical Assistance in Dying
Bibliographic record
Abstract
Unique reports of suicide and euthanasia date back more than 2 millennia, reflecting evolving philosophies of death and dying as expressions of the mores dominating a given era. One longstanding theme in the history of decisions to die has been staunch opposition founded in religious claims that one’s body is a trust from the divine (and therefore not wholly in their ownership). The role of the physician has also been traditionally estranged from participation in such decisions, dating back to rudimentary conceptions of medical ethics in the Hippocratic notion primum non nocere (‘first, do no harm’). However, fundamental principles in the modern philosophy of medicine lend support to the idea that physicians can be justified in actions which cause some harm, in so far as they are acting to fulfil a greater ethical imperative. This brief historical review explores the inception of modern North American medical assistance in dying (MAiD) policy through a series of critical case studies in the unfolding of its practice. Medically assisted dying has presently been legalised in Canada and some United States jurisdictions, but with critical caveats surrounding circumstances of mature minors, advance directives and mental illness as participants’ sole underlying medical condition. While the modern regulations surrounding MAiD continue to take shape, the palliative care community is well-positioned to both guide and scrutinise the ethics of this practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".