Factors associated with the adoption of a digital health service by patent proprietary medicine vendors (PPMVs) in Lagos, Nigeria
Bibliographic record
Abstract
Background Patent proprietary medicine vendors (PPMVs) are the first point of care for low-income Nigerian households. They are likely to have an important role in a digital care pathway established for low-income Nigerian women and children. Yet, little is known about what drives the adoption of digital platforms by PPMVs. Methods This study explores factors associated with the adoption of a digital service, NaijaCare, created to enable PPMVs to increase the range and quality of products and services they offer. A structured, quantitative, face-to-face survey was conducted among 248 PPMVs in Lagos in February and March 2020. Multivariate analysis was conducted to identify factors associated with the adoption of NaijaCare. Results Women comprise the majority (67%) of medicine vendors in Lagos. Most medicine vendors (64%) had gotten health training on the job. About a quarter (27%) of medicine vendors reported seeking business advice on the internet. Medicine vendors who had obtained on-the-job training had a 12.31 times higher odds ratio ( p < 0.01) of adopting the digital service. Medicine vendors who sought business advice on the internet had a 6.48 times higher odds ratio ( p < 0.001) of adopting NaijaCare. Conclusion The study findings suggest that PPMVs’ use of the digital service was driven by their desire to increase business profits. Digital care pathways targeting low-income households should be aligned with the business interests of informal providers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".