Quality of Community Pharmacy Practice in Antibiotic Self-Medication Encounters: A Simulated Patient Study in Upper Egypt
Bibliographic record
Abstract
Antibiotic misuse, either by patients or healthcare professionals, is one of the major contributing factors to antimicrobial resistance. In many Middle Eastern countries including Egypt, there are no strict regulations regarding antibiotic dispensing by community pharmacies. In this study, we examined antibiotic dispensing patterns in Egyptian community pharmacies. About 150 community pharmacies were randomly chosen using convenience sampling from the five most populous urban districts of Minia Governorate in Egypt. Two simulated patient (SP) scenarios of viral respiratory tract infection requiring no antibiotic treatment were used to assess the actual antibiotics dispensing practice of. Face-to-face interviews were then conducted to assess the intended dispensing practice. Descriptive statistics were calculated to report the main study outcomes. In 238 visits of both scenarios, 98.3% of service providers dispensed amoxicillin. Although stated otherwise in interviews, most pharmacy providers (63%) dispensed amoxicillin without collecting relevant information from presenting SPs. Findings showed high rates of antibiotic misuse in community pharmacies. Discrepancies between interviews and patient simulation results also suggest a practice‒knowledge gap. Corrective actions, whether legislation, enforcement, education, or awareness campaigns about antibiotic misuse, are urgently needed to improve antibiotic dispensing practices in Egyptian community pharmacies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".