Global Perspectives on Physician-Assisted Death: A Cross-Sectional Survey of Doctors’ Opinions
Bibliographic record
Abstract
Introduction Physician-assisted death (PAD) remains ethically and legally contested worldwide. PAD encompasses both euthanasia and physician-assisted suicide, and is used interchangeably with assisted dying. While some countries have legalized PAD, many others continue to deliberate, some remain silent, and many don't engage on this issue. Physicians' perspectives are central to these discussions, yet cross-national data remain limited. This study seeks to explore these perspectives and identify the factors that influence them. Methods We conducted an anonymous, online cross-sectional survey of physicians' opinions across 17 countries (n = 107). The questionnaire assessed demographics, clinical background, experiences with PAD and ethical/legal attitudes using Likert scales, checkboxes and free-text responses. This was disseminated through professional networks using online platforms. Results Of the 107 respondents, 55 (51.4%) practiced in Asia, 42 (39.3%) in the United Kingdom, and 10 (9.3%) in the United States, Europe, Oceania or Africa. Over half (n = 55, 51.9%) were aged between 25-34 years and nearly a quarter (n = 25, 23.4%) had more than 20 years of clinical experience across diverse specialties. Majority (n = 58, 54.2%) supported legalization of PAD for mentally competent, terminally ill adults, with declining support for non-terminal physical suffering (n = 47, 43.9%) and psychological suffering (n = 27, 25.2%). While views diverged on whether PAD undermines doctor-patient trust, most agreed it could coexist with palliative care. Legislative preferences varied widely, with leading support for legalizing PAD with strict safeguards (n = 39, 36.4%), while 15 (14.0%) preferred that it remain illegal. Discussion The findings of our survey align with published single-country studies which showed that majority supported PAD in restricted contexts. Ethical, cultural and professional concerns persist, with tension between autonomy and non-maleficence being a central theme. There seems to be a notable cross-sectional diversity with strong opposition from Northeast Asia and conservative regions and more openness in the West. These results should be interpreted considering study limitations, including the relatively small sample size and uneven representation across countries. Conclusion Doctors across multiple countries demonstrate nuanced but generally supportive views of PAD under strict 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".