Call for elimination program of Malaria among children under 5 years old living in refugee camps in eastern Democratic Republic of Congo
Bibliographic record
Abstract
Malaria is a significant public health challenge in the Democratic Republic of the Congo (DR Congo), with a high prevalence and mortality rate, particularly among children under five years old. This study examines the impact of malaria in eastern DR Congo, where armed conflict and humanitarian crises have resulted in overcrowded refugee and internally displaced persons (IDP) camps, exacerbating malaria transmission. Malnutrition, limited access to healthcare, and poor living conditions makes children under the age of five particularly vulnerable. Despite attempts by organizations such as the World Health Organization and various non-governmental organizations to supply insecticide-treated bed nets and antimalarial drugs, implementation in refugee camps is impeded by war, resource constraints, and insufficient healthcare facilities. A focused elimination approach that includes integrated vector control, enhanced diagnostic access, healthcare professional training, and community engagement is critical. Surveillance and research are critical for determining malaria prevalence and resistance patterns. Effective malaria treatment in refugee camps necessitates broad strategies to protect vulnerable children and lower disease burdens.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".