Provider knowledge of treatment policy and dosing regimen with artemether-lumefantrine and quinine in malaria-endemic areas of western Kenya
Bibliographic record
Abstract
BACKGROUND: Due to widespread anti-malarial drug resistance in many countries, Kenya included, artemisinin-based Combination Therapy (ACT) has been adopted as the most effective treatment option against malaria. Artemether-lumefantrine (AL) is the first-line ACT for treatment of uncomplicated malaria in Kenya, while quinine is preferred for complicated and severe malaria. Information on the providers' knowledge and practices prior to or during AL and quinine implementation is scanty. The current study evaluated providers' knowledge and practices of treatment policy and dosing regimens with AL and quinine in the public, private and not-for-profit drug outlets. METHODS: A cross-sectional survey using three-stage sampling of 288 (126 public, 96 private and 66 not-for-profits) providers in drug outlets was conducted in western Kenya in two Plasmodium falciparum-endemic regions with varying malarial risk. Information on provider in-service training, knowledge (qualification, treatment policy, dosing regimen, recently banned anti-malarials) and on practices (request for written prescription, prescription of AL, selling partial packs and advice given to patients after prescription), was collected. RESULTS: Only 15.6% of providers in private outlets had received any in-service training on AL use. All (100%) in public and majority (98.4%) in not-for-profit outlets mentioned AL as first line-treatment drug. Quinine was mentioned as second-line drug by 47.9% in private outlets. A total of 92.0% in public, 57.3% in private and 78.8% in not-for-profit outlets stated correct AL dose for adults. A total of 85.7% of providers in public, 30.2% in private and 41.0% in not-for-profit outlets were aware that SP recommendations changed from treatment for mild malaria to IPTp in high risk areas. In-service training influenced treatment regimen for uncomplicated malaria (P = 0.039 and P = 0.039) and severe malaria (P < 0.0001 and P = 0.002) in children and adults, respectively. Most (82.3%) of private outlets sell partial packs of AL while 72.4% do not request for written prescription for AL. In-service training influenced request for written prescription (P = 0.001), AL prescription (P < 0.0001) and selling of partial packs (P < 0.0001). CONCLUSION: Public-sector providers have higher knowledge on treatment policy and dosing regimen on recommended anti-malarials. Changes in treatment guidelines should be accompanied by subsequent implementation activities involving all sector players in unbiased strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".