Bibliographic record
Abstract
This is the last of four articles Edited by War affects human health through the direct violence of bombs and bullets, the disruption of economic and social systems by which people use to address their health needs, the famine and epidemics that follow such disruptions, and the diversion of economic resources to military ends rather than health needs.1–8 In recent years war has been framed as a public health problem.9 This highlights the role of health workers in preventing and mitigating destructiveness but also raises questions regarding the constraints to their achievement of such objectives. #### Summary points Health work in zones of conflict can initiate and spread peace through conflict management, solidarity with indigenous health workers, strengthening of the social fabric, public dissent and restriction of the destructiveness of war Evaluation tools need refinement, but there is preliminary evidence of effectiveness for some health-peace initiatives The transition towards peace in war-affected zones will often improve health care and health status of populations. But do health workers have a role in expanding peace? Progress towards more peaceful relationships, between large entities such as nations or blocs, or small entities like community groups, requires multitrack actions at several levels. Does health care offer one such track? Only empirical data will answer this question, but our preliminary analysis of information suggests that health initiatives have indeed been successfully used as peace initiatives.10–12 This paper seeks to briefly elaborate on the linkage between health and peace in the hope that others will see useful applications of this linkage. We use the term “health-peace initiative” to refer to any initiative that is intended to improve the health of a population and to simultaneously heighten that population's level of peace and security. The five peace building mechanisms described below have been …
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".