Awareness of Antimalarial Policy and Use of Artemisinin-Based Combination Therapy for Malaria Treatment in Communities of Two Selected Local Government Areas of Ogun State, Nigeria
Bibliographic record
Abstract
With limited data on the awareness of changes in the use of antimalaria drugs and availability and use of artemisinin-based combination therapy (ACT) in the context of the Roll Back Malaria (RBM) program, we conducted this descriptive cross-sectional study of 262 registered women attending antenatal clinics and 233 mothers of under-five children. We used a questionnaire to assess the awareness, availability and use of ACT in Ijebu North and Yewa North Local Government Areas (LGAs) of Ogun State. Malaria is holo-endemic in these areas, and the RBM program has been implemented for years prior to the 2010 RBM deadline. Data were also collected through focus group discussions, along with secondary data from hospital records. Hospital stock records showed inadequate and inconsistent supplies of ACT drugs in hospitals surveyed. Only 23.0% of respondents knew about ACT drugs. About 48% preferred analgesics over ACT drugs (0.6%) for malaria treatment. Lack of awareness was the major reason for non-use of ACT drugs (86.1%). Communities in Yewa North had more supplies of ACT drugs and knew more about ACT than those in Ijebu North. Adequate information on ACT needs to be made available and accessible under a public-private partnership if 2010 RBM targets (now past) and the 2015 Millennium Development Goal (ongoing) for malaria are to be realized in the study communities and Ogun State in general.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".