Idealism is not enough: designing peace into medical education
Bibliographic record
Abstract
Purpose – Physicians' uniquely privileged social status gives them influence to help prevent conflict in addition to treating its victims. Yet the peacebuilding role of physicians has received little attention in medical education. In this paper, the authors tackle both and provide some concrete guidance to medical schools interested in taking it on. Design/methodology/approach – Using Qualitative Description, a review of literature and expert interviews in violence prevention, peacebuilding, medicine and medical education, three statements are posited: improved healthcare may enhance the prospects for peace; there are mechanisms by which healthcare may potentially enhance peacebuilding; and medical education can be designed to support these mechanisms. A “peace audit” is developed against which to evaluate the efforts of medical schools towards peacebuilding. This audit is used to assess a medical school in Nepal that is invested in peacebuilding. Findings – Medicine has a role, both in resolving conflict, and in preventing its occurrence. The experts believe that physicians have a responsibility to go further than treating the wounded and address the root cause of conflict: the structural violence of poverty and economic disparity. Originality/value – This paper considers the mechanisms by which medicine supports peacebuilding, and the consequences of this for medical education. The literature to date has not dealt with this issue.
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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 | 0.063 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".