Malaria rapid diagnostic tests in community pharmacies in Rwanda: availability, knowledge of community pharmacists, advantages, and disadvantages of licensing their use
Bibliographic record
Abstract
Abstract Background Presumptive treatment of malaria is often practiced in community pharmacies across sub-Saharan Africa (SSA).To address this issue, the World Health Organization (WHO) recommends that malaria Rapid Diagnostic Tests (m-RDTs) be used in these settings, as they are used in the public sector. However, their use remains unlicensed in the community pharmacies in Rwanda. This can lessen their availability and foster presumptive treatment. Therefore, this study investigated the availability of m-RDTs, knowledge of community pharmacists on the use of m-RDTs, and explored Pharmacists’ perceptions of the advantages and disadvantages of licensing the use of m-RDTs in community pharmacies. Methods This was a cross-sectional study among 200 licensed community pharmacists who were purposefully sampled nationwide from 11th February to 12th April 2022. Data was collected using an online data collection instrument composed of open-ended and closed-ended questions. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 25.0. The chi-square test was used to evaluate the association between the availability of m-RDTs and independent variables of interest. Content analysis was used for qualitative data. Results Although 59% were consulted by clients requesting to purchase m-RDTs, only 27% of the participants had m-RDTs in stock, 66.5% had no training on the use of m-RDTs, and 18.5% were not at all familiar with using the m-RDTs. Most of the participants (91.5%) agreed that licensing the use of m-RDTs in community pharmacies could promote the rational use of antimalarials. The chi-square test indicated that being requested to sell m-RDTs (x 2 = 6.95, p = 0.008), being requested to perform m-RDTs (x 2 = 5.39, p = 0.02),familiarity using m-RDTs (x 2 = 17.24, p = 0.002), availability of a nurse in the Pharmacy (x 2 = 11.68, p < 0.001), and location of the pharmacy (x 2 = 9.13, p = 0.048) were all significantly associated with the availability of m-RDTs in the pharmacy. Conclusions The availability of m-RDTs remains low in community pharmacies in Rwanda, and less training is provided to community pharmacists regarding the use of m-RDTs. Nevertheless, community pharmacists had positive perceptions of the advantages of licensing the use of m-RDTs. Thus, licensing the use of m-RDTs is believed to be the first step toward promoting the rational use of antimalarial medicines in Rwanda.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".