Physicians' personal end-of-life preferences and their connection to clinical practice : an international multi-method study
Bibliographic record
Abstract
Physicians play a key role in end-of-life decisions, which are of increasing importance as the global aging population expands and people live longer with extended illnesses. In developed countries, half of all deaths are preceded by at least one end-of-life decision, including withholding and withdrawing treatment, palliative sedation or assisted dying, among others. There is evidence to suggest a connection between physicians’ personal preferences and their own clinical practice. However, there is a lack of knowledge on physicians’ end-of-life decision preferences and how those preferences impact their clinical decision-making. Exploring this link is crucial as physicians have significant influence on patients and health care systems. Gaining a better understanding will require exploring various factors including cultural, religious, sociocultural and jurisdictional influences. The PROPEL study (Physician Reported Preferences for End-of-Life) seeks to gain an in-depth understanding of physicians’ personal preferences on end-of-life decisions, how preferences impact their clinical practice and how various factors influence preferences. A multi methods approach is used including a quantitative survey in five countries: Belgium, Italy, Canada, USA (Georgia, Oregon & Wisconsin) and Australia (Queensland & Victoria); combined with a qualitative exploration using semi-structured interviews in Belgium, Italy and the USA (Wisconsin). As end-of-life options are expanding around the world, exploring these issues will allow for international comparison and knowledge sharing that will address complex moral and clinical issues related to end-of-life care.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".