Associations between physicians’ personal preferences for end-of-life decisions and their own clinical practice: PROPEL survey study in Europe, North America, and Australia
Bibliographic record
Abstract
BACKGROUND: Physicians have significant influence on end-of-life decisions. Therefore, it is important to understand the connection between physicians' personal end-of-life care preferences and clinical practice, and whether there is congruence between what they prefer for themselves and for patients. AIM: Study to what extent physicians believe their personal end-of-life preferences impact their clinical practice and to what extent physicians' personal treatment option preferences differ from what they prefer for their patients. DESIGN: A cross-sectional survey was conducted from May 2022 to February 2023. SETTING/PARTICIPANTS: Eight jurisdictions: Belgium, Italy, Canada, USA (Oregon, Wisconsin, and Georgia), and Australia (Victoria and Queensland). Three physician types were included: general practitioners, palliative care physicians, and other medical specialists. RESULTS: We analyzed 1157 survey responses. Sixty-two percent of physicians acknowledge considering their own preferences when caring for patients at the end of life and 29.7% believe their personal preferences impact the recommendations they make. Palliative care physicians are less likely to consider their own preferences when caring for and making recommendations to patients. Congruence was found between what physicians prefer for patients and themselves with cardiopulmonary resuscitation considered "not a good option for both" by 99.1% of physicians. Incongruence was found with physicians considering some options "not good for the patient, but good for themselves"-palliative sedation (8.3%), physician-assisted suicide (7.0%), and euthanasia (11.6%). CONCLUSION: Physicians consider their own preferences when providing care and their preferences impact the recommendations they make to patients. Incongruence exists between what physicians prefer for themselves and what they prefer for patients.
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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 | 0.003 | 0.009 |
| 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) | 0.001 | 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".