The Influence of Geography, Religion, Religiosity and Institutional Factors on Worldwide End-of-Life Care for the Critically Ill: The WELPICUS Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between provider religion and religiosity and consensus about end-of-life care and explore if geographical and institutional factors contribute to variability in practice. METHODS: Using a modified Delphi method 22 end-of-life issues consisting of 35 definitions and 46 statements were evaluated in 32 countries in North America, South America, Eastern Europe, Western Europe, Asia, Australia and South Africa. A multidisciplinary, expert group from specialties treating patients at the end-of-life within each participating institution assessed the association between 7 key statements and geography, religion, religiosity and institutional factors likely influencing the development of consensus. RESULTS: Of 3049 participants, 1366 (45%) responded. Mean age of respondents was 45 ± 9 years and 55% were females. Following 2 Delphi rounds, consensus was obtained for 77 (95%) of 81 definitions and statements. There was a significant difference in responses across geographical regions. South African and North American respondents were more likely to encourage patients to write advance directives. Fewer Eastern European and Asian respondents agreed with withdrawing life-sustaining treatments without consent of patients or surrogates. While respondent's religion, years in practice or institution did not affect their agreement, religiosity, physician specialty and responsibility for end-of-life decisions did. CONCLUSIONS: Variability in agreement with key consensus statements about end-of-life care is related primarily to differences among providers, with provider-level variations related to differences in religiosity and specialty. Geography also plays a role in influencing some end-of-life practices. This information may help understanding ethical dilemmas and developing culturally sensitive end-of-life care strategies.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".