The WEIRD Trio: The Cultural Gap between Physicians, Learners, and Patients in Pluralistic Societies
Bibliographic record
Abstract
Physicians are shaped by sociological and philosophical factors that often differ from those of their patients. This is of particular concern in pluralistic societies when navigating ethical disagreements because physicians often misunderstand or even dismiss patient perspectives as being irrational. This paper examines these factors and why many physicians approach ethics as they do while elucidating various patient perspectives and demonstrating how they make sense when considered from a different cultural worldview. Many physicians are trained in contexts that are WEIRD: Western, educated, industrialized, rich, and democratic. These sociological characteristics tend to go hand in hand with the trio of individualism, secularism, and existentialism. These then shape an approach to ethics that focuses on the individual patient, makes no reference to the divine, and focuses on a patient's personal desires. This contrasts significantly with many patients who are collectivistic or religious, and then make rational decisions based on other values. The social fact of pluralism implores physicians to temper confidence in their own cultures while considering others to promote mutual understanding and improved care. This paper concludes with a discussion of how bridges can be built across cultures without sliding into relativism, beginning with recognizing and communicating our shared moral intuitions.
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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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.086 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.010 |
| 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".