VAN DUZER MEMORIAL ORATION: THE EFFECT OF ARMED CONFLICT ON CHILD HEALTH AND DEVELOPMENT
Bibliographic record
Abstract
Armed conflict has occurred in many parts of the world for centuries and undoubtedly will occur in the future. In the past, combatants suffered; now the majority of suffering is by civilians. Children in these regions are denied the benefits of health care and normal nurturing both during and especially for prolonged periods after the conflict (when the health and social systems have been disrupted or are non‐existent). Their problems of health and development are major; problems for which the experience and knowledge of pediatrics and pediatric research could contribute. Yet, to date, the study of the health and development of children in war zones has not been a major priority of pediatric societies or of the large community of pediatric clinicians and researchers. Recently the Programme for Global Pediatric Research has held meetings with representatives of agencies working in areas of armed conflict together with pediatric clinicians and researchers. They explored the health and developmental problems of children in war zones. Recommendations from those meetings highlighted the plight of mothers and children during conflict and in the period “after the shooting stops.” Child health and development is critically affected during these times. In many instances planning has been inadequate and both government and legal support have been deficient. This presentation will describe the health and developmental problems of children in zones of armed conflict and steps to be taken to alleviate these major problems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".