The promises and limitations of codes of medical ethics as instruments of policy change
Bibliographic record
Abstract
Codes of medical ethics (codes) are part of a longstanding tradition in which physicians publicly state their core values and commitments to patients, peers, and the public. However, codes are not static. Using the historical evolution of the Canadian Medical Association's Code of Ethics as an illustrative case, we argue that codes are living, socio-historically situated documents that comprise a mix of prescriptive and aspirational content. Reflecting their socio-historical situation, we can expect the upheaval of the COVID-19 pandemic to prompt calls to revise codes. Indeed, Alex John London has argued in favour of specific modifications to the World Medical Association's International Code of Medical Ethics (which has since been revised) in light of moral and scientific failures that occurred during the COVID-19 pandemic. Responding to London, we address the more general question: should codes be modified to reflect lessons drawn from the COVID-19 pandemic or future such upheavals? We caution that codes face limitations as instruments of policy change because they are inherently interpretive and 'multivocal', that is, they usually underdetermine or provide more than one answer to the question, 'What should I do now?' Nonetheless, as both prescriptive and aspirational documents, codes also serve as tools for reflection and deliberation-collective practices that are necessary to engaging with and addressing the moral and scientific uncertainties inherent to medicine.
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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 | 0.303 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.173 |
| Scholarly communication | 0.036 | 0.066 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.026 | 0.044 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".