Physicians’ preferences for their own end of life: a comparison across North America, Europe, and Australia
Bibliographic record
Abstract
OBJECTIVE: To study physicians' personal preferences for end-of-life practices, including life-sustaining and life-shortening practices, and the factors that influence preferences. DESIGN: A cross-sectional survey (May 2022-February 2023). SETTING: Eight jurisdictions: Belgium, Italy, Canada, USA (Oregon, Wisconsin, and Georgia), Australia (Victoria and Queensland). PARTICIPANTS: Three physician types: general practitioners, palliative care physicians, and other medical specialists. MAIN OUTCOME MEASURES: Percentage of physicians who preferred various end-of-life practices and provided information about influence on preferences and demographics. RESULTS: 1157 survey responses were analysed. Physicians rarely considered life-sustaining practices a (very) good option (in cancer and Alzheimer's respectively: cardiopulmonary resuscitation, 0.5% and 0.2%; mechanical ventilation, 0.8% and 0.3%; tube feeding, 3.5% and 3.8%). About half of physicians considered euthanasia a (very) good option (respectively, 54.2% and 51.5%). The proportion of physicians considering euthanasia a (very) good option ranged from 37.9% in Italy to 80.8% in Belgium (cancer scenario), and 37.4% in Georgia, USA to 67.4% in Belgium (Alzheimer's scenario). Physicians practising in a jurisdiction with a legal option for both euthanasia and physician-assisted suicide were more likely to consider euthanasia a (very) good option for both cancer (OR 3.1, 95% CI 2.2 to 4.4) and Alzheimer's (OR 1.9, 95% CI 1.4 to 2.6). CONCLUSION: Physicians largely prefer to intensify alleviation of symptoms at the end of life and avoid life-sustaining techniques. In a scenario of advanced cancer or Alzheimer's disease, over half of physicians prefer assisted dying. Considerable preference variation exists across jurisdictions, and preferences for assisted dying seem to be impacted by the legalisation of assisted dying within jurisdictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".