Interactions between patent medicine vendors and customers in urban and rural Nigeria
Bibliographic record
Abstract
Patent medicine vendors (PMVs) supply a large portion of the drugs used by the public in African countries to treat their illnesses. Little has been reported about what actually transpires between PMVs and their customers, but nevertheless, concerns have been raised about the potential for abuse of their position. This study conducted 720 observations of PMV-customer interaction in 444 medicine shops in both the metropolis of Ibadan and the rural town of Igbo-Ora in Oyo State, Nigeria. Each interaction lasted 2 minutes on average. A quarter of the customers shared their illness problems with the shop attendant, 9% presented a prescription and the majority simply requested items for purchase. Most customers (73%) were buying drugs for themselves, while the remainder had been sent to purchase for another person. The former were more likely to be adults, while the latter were more often children and adolescents. The most common PMV behaviours are: selling the requested medicine (69%), giving their own suggestions to the customer (30%), asking questions about the illness (19%) and providing instructions on how to take the medicine (21%). Only three referrals were observed. The large number of specific drug requests was evidence of a public that was actively involved in self-care, and thus the major role of the PMV appeared to be one of salesperson meeting that need. A second role became evident when the customer actually complained about his/her illness, a practice associated with the more active PMVs who asked questions, gave suggestions and provided information. These PMV roles can be enhanced through consumer education, PMV training and policy changes to standardize and legitimize PMV contributions to primary health care.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".