Bibliographic record
Abstract
One third of the world's malaria deaths occur in Nigeria. It is doubtful whether Nigeria will meet the malaria control target of the Millennium Development Goals by 2015, having failed to meet the Abuja target to halve the burden of malaria by 2010. This paper assesses the current malaria burden and progress toward malaria control. Substantial data were obtained from the 2008 Nigeria Demographic and Health Survey and other secondary sources. Data showed that the malaria burden is still enormous because of inadequate control efforts. In 2008, only 17% of Nigerians owned at least one net, compared with 12% in 2003. Eight percent owned an insecticide-treated mosquito net (ITN), but only 6% of under-five children and 5% of pregnant women slept under an ITN. Only one third of under-five children with fever received antimalarial drugs, while one fifth of pregnant women took antimalarial drugs for prevention. Chloroquine is still the most common drug used in malaria treatment, despite its ban in first-line treatment since 2005. The paper concludes that scaling up home management of malaria and a community-centred approach to ITN and artemisinin-based combination therapy provisioning should be prioritized.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".