Knowledge, attitudes and practices survey on antimicrobial resistance and stewardship among pharmacy healthcare workers in 28 African countries
Bibliographic record
Abstract
INTRODUCTION: Antimicrobial resistance (AMR) is a pressing global health problem disproportionately affecting low- and middle-income countries. Inappropriate antimicrobial prescription and use exacerbate AMR. This study assesses the knowledge, attitudes and practices (KAP) toward AMR and antimicrobial stewardship (AMS) among pharmacy healthcare workers involved in antimicrobial dispensing across 28 African countries. METHODS: An online survey was distributed to collect data on KAP from HCWs who dispense antimicrobials in African countries. Responses were scored, and a 70% cut-off mark was used to differentiate between good and poor KAP. Logistic regression analysis was used to identify factors associated with good or poor KAPs. RESULTS: A total of 2567 responses (40%) were received, of which 908 were from pharmacy HCWs who dispensed antibiotics in 28 countries. Of the 908 eligible respondents, 71.3% had good knowledge of AMR and AMS, 59.9% displayed good attitudes towards the burden of AMR and appropriate prescription of antimicrobials and 41.6% displayed good practices related to AMS. Patient demands and influence from pharmaceutical companies were among the factors that influenced the dispensing of antibiotics. In multivariable logistic regression, licensed pharmacists were more likely to have good knowledge of AMR than pharmacy technicians (adjusted OR (aOR) 1.78; 95% CI 1.64 to 1.93). Male dispensers were less likely to have a positive attitude towards AMR than female dispensers (aOR 0.69, 95% CI 0.51 to 0.94). Moreover, dispensers affiliated with public health facilities demonstrated better AMR practices than those affiliated with private facilities. Overall, good AMR knowledge status was significantly associated with positive attitudes (χ²=97.1, p<0.001) and practices (χ²=6.5, p<0.05) of AMR. CONCLUSION: This study revealed limited understanding of AMR among dispensers without formal pharmaceutical training and a positive association between good knowledge and positive attitudes and practices. The findings underscore the importance of providing workplace educational materials on AMR and AMS to build capacity in healthcare institutions and promote proper antibiotic dispensing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".