Bibliographic record
Abstract
Despite the increase in and evolving nature of armed conflicts, the ethical issues faced by military physicians working in such contexts are still rarely examined in the bioethics literature. Military physicians are members of the military, even if they are non-combatants; and their role is one of healer but also sometimes humanitarian. Some scholars wonder about the moral compatibility of being both a physician and soldier. The ethical conflicts raised in the literature regarding military physicians can be organized into three main perspectives: 1) moral problems in military medicine are particular because of the difficulty of meeting the requirements of traditional bioethical principles; 2) medical codes of ethics and international laws are not well adapted to or are too restrictive for a military context; and 3) physicians are social actors who should either be pacifists, defenders of human rights, politically neutral or promoters of peace. A review of the diverse dilemmas faced by military physicians shows that these differ substantially by level (micro, meso, macro), context and the actors involved, and that they go beyond issues of patient interests. Like medicine in general, military medicine is complex and touches on potentially contested views of the roles and obligations of the physician. Greater conceptual clarity is thus needed in discussions about military medical ethics.
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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 | 0.127 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.012 | 0.151 |
| Scholarly communication | 0.034 | 0.079 |
| Open science | 0.011 | 0.017 |
| Research integrity | 0.029 | 0.059 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".