Implementation of the Revised National Malaria Control Guidelines: Compliance and Challenges in Public Health Facilities in a Southern Nigerian State
Bibliographic record
Abstract
Background: There has been a concerted effort to reduce malaria burden and bring malaria related mortality to zero. The objectives of this survey were to assess the level of adherence to the current revised malaria control guidelines in the public health facilities in Cross River State of Nigeria and to identify the challenges as well as suggest ways for improvement in treatment outcomes. Methods: This was a mixed observational and qualitative survey conducted in 32 public health facilities from 21st to 25th June 2022. Treatment records on malaria were assessed for adherence to the National guidelines. In-depth interviews were conducted with 36 key informants and 4 purposefully selected stakeholders to identify the successes and challenges. Quantitative data were summarized and presented in simple proportions and percentages while qualitative information was recorded, the transcripts thematically coded, analyzed and presented using NVivo 11 software. Results: The survey revealed that vector control program was poorly implemented across the state. For case management, presumptive treatment was frequently practiced especially at secondary health facilities for uncomplicated malaria. More than 60% of uncomplicated malaria were being treated with parenteral artemether instead of oral artemisinin combination therapy (ACTs) as recommended. Severe malaria were not treated with Intravenous (IV) Artesunate as first line drug in about 40% of the secondary health facilities. Key successes were noted in malaria management in pregnancy. Major challenges identified include: stock out of commodities, shortage of clinical man power, and low trust in parasitological diagnosis. Conclusion: The survey showed that adherence to the key recommendations in various categories of malaria control among health care providers in the public health facilities was below expectation. Malaria preventive treatment in pregnancy with SP fared better perhaps because of its inclusion in ANC packages.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".