Physician-Assisted Suicide and Euthanasia: Can You Even Imagine Teaching Medical Students How to End Their Patients' Lives?
Bibliographic record
Abstract
The peer-reviewed literature includes numerous well-informed opinions on the topics of euthanasia and physician-assisted suicide. However, there is a paucity of commentary on the interface of these issues with medical education. This is surprising, given the universal assumption that in the event of the legalization of euthanasia, the individuals on whom society expects to confer the primary responsibility for carrying out these acts are members of the medical profession. Medical students and residents would inevitably and necessarily be implicated. It is my perspective that everyone in the profession, including those charged with educating future generations of physicians, has a critical interest in participating in this ongoing debate. I explore potential implications for medical education of a widespread sanctioning of physician-inflicted and physician-assisted death. My analysis, which uses a consequential-basis approach, leads me to conclude that euthanasia, when understood to include physician aid in hastening death, is incommensurate with humanism and the practice of medicine that considers healing as its overriding mandate. I ask readers to imagine the consequences of being required to teach students how to end their patients' lives and urge medical educators to remain cognizant of their responsibility in upholding long-entrenched and foundational professional values.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.015 |
| 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".