Bibliographic record
Abstract
The eastern Democratic Republic of the Congo (DRC) grapples with entrenched armed conflicts, creating a complex humanitarian crisis with far-reaching implications for global health. This paper explores the intersection between armed conflict in the region and the risks of zoonotic disease transmission, shedding light on interconnected challenges and proposing integrated strategies for mitigation. Armed conflict disrupts healthcare systems, affecting healthcare facilities (HCF) and healthcare workers (HCW), destroying millions of lives, impoverishing communities, and weakening surveillance systems. This deleterious situation is a bottleneck to achieving the Sustainable Development Goals (SDGs), especially Universal Health Coverage (UHC), as it prevents millions of Congolese from accessing healthcare services. The direct impact of armed insecurity undermines Global Health Security (GHS) by fostering natural habitat degradation and biodiversity loss, exacerbating vulnerabilities to zoonotic disease outbreaks. Forced population displacement and encroachment on natural habitats amplify human-wildlife interaction, facilitating zoonotic disease spillover and increasing the risk of regional and global spread. Biodiversity loss and poaching further compound these challenges, underscoring the need for holistic approaches that address both conservation and public health concerns. Mitigating zoonotic disease risks requires strengthening surveillance systems, promoting community engagement, and integrating conservation efforts with conflict resolution initiatives. By adopting a comprehensive approach, including the incorporation of One Health considerations in all peace-seeking and humanitarian efforts, stakeholders can enhance Global Health Security, scale up UHC, and promote sustainable development in conflict-affected regions. Creativity and strategic foresight are essential to safeguarding the well-being of human, livestock, plant, and wildlife populations in the Eastern DRC<i>.</i>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".