Health Diplomacy as a Tool to build resilient health systems in Conflict Settings – A Case of Sudan
Bibliographic record
Abstract
Attack on health has become a significant concern for non-belligerents of war, including healthcare personnel and facilities as witnessed in the ongoing Sudan conflict. About 1.5 billion people living in fragile and conflict-affected settings (FCAS) have a heightened need for essential health services. Conflicts often lead to the disruption of building blocks of health systems, lack of access to health facilities, failure of essential medical supply chains, the collapse of political, social and economic systems, migration of health care workers, and upsurges in illness. While health indicators often decline in conflict, health can also bring peace and harmony among communities. Investment in health systems and health diplomacy is a neutral starting point for bringing peace and mitigating conflicts. The international commitment towards Sustainable Development Goals (SDGs) provides the impetus to emphasize the relationship between health and peace with the amalgamation of SDG 3, SDG 16, and SDG 17. The inspection of how health should be used as a ‘tool for peace’ and not as leverage during the war must be reiterated by international institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".