Are Military and Medical Ethics Necessarily Incompatible? A Canadian Case Study
Bibliographic record
Abstract
Military physicians are often perceived to be in a position of 'dual loyalty' because they have responsibilities towards their patients but also towards their employer, the military institution. Further, they have to ascribe to and are bound by two distinct codes of ethics (i.e., medical and military), each with its own set of values and duties, that could at first glance be considered to be very different or even incompatible. How, then, can military physicians reconcile these two codes of ethics and their distinct professional/institutional values, and assume their responsibilities towards both their patients and the military institution? To clarify this situation, and to show how such a reconciliation might be possible, we compared the history and content of two national professional codes of ethics: the Defence Ethics of the Canadian Armed Forces and the Code of Ethics of the Canadian Medical Association. Interestingly, even if the medical code is more focused on duties and responsibility while the military code is more focused on core values and is supported by a comprehensive ethical training program, they also have many elements in common. Further, both are based on the same core values of loyalty and integrity, and they are broad in scope but are relatively flexible in application. While there are still important sources of tension between and limits within these two codes of ethics, there are fewer differences than may appear at first glance because the core values and principles of military and medical ethics are not so different.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.044 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".