Public health consequences of armed conflict in Sudan in the face of global donor fatigue
Bibliographic record
Abstract
Sudan, a country located in northeastern Africa, is grappling with a profound health crisis resulting from the recent war that erupted on April 15, 2023. This conflict has inflicted significant damage on the country's health system, particularly through the destruction of healthcare infrastructure. Approximately 61% of health facilities have been destroyed, leaving only 16% operating optimally in Khartoum. Hospitals and clinics, vital for public health, have become targets of violence, exacerbating the challenges faced by the nation. The World Health Organization has noted the closure of roughly 16 hospitals since the start of the conflicts due to staff safety concerns as well as a shortage of hospital supplies, consumables, and medication. There has also been a gradual waning of donor support and resources allocated to address protracted crises and emergencies worldwide Sudan receives very little funding from donor organizations to maintain its healthcare system, which worsens the nation's general public health architecture. This makes the country vulnerable to serious challenges like disease outbreaks, starvation, infectious diseases, deteriorating health infrastructure, and mental health issues. To successfully reduce the severity of negative impacts on public health, the crisis must be ceased and facilities reopened. An emergency disease surveillance system for infectious diseases should be established, women and child health should be prioritized, and mental health awareness programs and services should be implemented. The global community must support the efforts to mitigate the devastating effects of this crisis. This article aims to highlight the critical impact of the recent war on Sudan's healthcare, advocating for urgent measures, including facility reopening, disease surveillance, and global support to mitigate devastating consequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".