Private pharmacy staff in five main towns in Benin, Burkina Faso, and Mali: knowledge and practices concerning malaria care in 2014
Bibliographic record
Abstract
The Global Fund's involvement in the fight against malaria has led to significant improvements, but mostly through programs supporting public-sector health facilities and personnel. The authors report the results of the preliminary survey preceding their intervention with private pharmacies. A simple random sampling technique was used to select the sample of pharmacies in urban areas in Burkina Faso, Benin, and Mali. A pretested questionnaire was administered to the supervisor present in each pharmacy at the time of the survey. Data were collected by local students in the first quarter of 2014. In all, 94 pharmacies were surveyed, representing 17.6% of all the pharmacies in these 5 cities. Among the participants, 84% knew about the national malaria control program, and 77.7% about artemisinin-based combination therapy (ACT), while 38.8% knew the national protocols. Licensed pharmacists had a better knowledge of ACT than their assistants, and training improved knowledge of treatment for uncomplicated malaria episodes. These pharmacists and assistants would like to be more involved in the fight against malaria. They are ready to advise ACT when appropriate after rapid detection tests. It is necessary to find resources for subsidized inputs in the private sector to make these drugs and tests more accessible for all patients.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".