Over-the-counter antibiotic dispensing by pharmacies: a standardised patient study in Udupi district, India
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance is a global health emergency, and one of the contributing factors is overuse and misuse of antibiotics. India is one of the world's largest consumers of antibiotics, and inappropriate use is potentially widespread. This study aimed to use standardised patients (SPs) to measure over-the-counter antibiotic dispensing in one region. METHODS: Three adults from the local community in Udupi, India, were recruited and trained as SPs. Three conditions, in both adults and children, were considered: diarrhoea, upper respiratory tract infection and acute fever. Adult SPs were used as proxies for the paediatric cases. RESULTS: A total of 1522 SP interactions were successfully completed from 279 pharmacies. The proportion of SP interactions resulting in the provision of an antibiotic was 4.34% (95% CI 3.04% to 6.08%) for adult SPs and 2.89% (95% CI 1.8% to 4.4%) for child SPs. In the model, referral to another provider was associated with an OR 0.38 (95% CI 0.18 to 0.79), the number of questions asked was associated with an OR 1.54 (95% CI 1.30 to 1.84) and an SP-pharmacist interaction lasting longer than 3 min was associated with an OR 3.03 (95% CI 1.11 to 8.27) as compared with an interaction lasting less than 1 min. CONCLUSION: Over-the-counter antibiotic dispensing rate was low in Udupi district and substantially lower than previously published SP studies in other regions of India. Dispensing was lowest when pharmacies referred to a doctor, and higher when pharmacies asked more questions or spent more time with clients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".