Antibiotic dispensing practices and determinants among informal healthcare providers in low- and middle-income countries: a mixed-methods scoping review
Bibliographic record
Abstract
INTRODUCTION: Antimicrobial stewardship efforts in low- and middle-income countries (LMICs) largely focus on qualified practitioners, yet informal healthcare providers (IPs) deliver much of the primary care. Although these providers frequently dispense antibiotics, their practices remain poorly documented and are not captured in existing surveillance systems. METHODS: Using the Joanna Briggs Institute methodology, this scoping review synthesised evidence on antibiotic dispensing and its determinants among IPs in LMICs. Nine databases (MEDLINE, EMBASE, SCOPUS, Global Health, CINAHL, Web of Science, LILACS, African Journals Online via Africa-Wide Information and Index Medicus for the South-East Asia Region) were searched, yielding 12 095 records, of which 37 studies met the inclusion criteria. RESULTS: Across 31 studies reporting dispensing practices, antibiotic use by IPs varied widely: 18%-74% in studies using standardised methods, 5%-96% in provider-reported studies and 2%-86% in consumer-reported studies. Eight qualitative studies identified key behavioural and contextual determinants shaping dispensing, including limited knowledge, experience-based learning, patient expectations, peer and pharmaceutical influence, perceived consequences of withholding antibiotics and economic incentives. CONCLUSION: Antibiotic dispensing by IPs is widespread and represents a large but unmeasured component of antibiotic use in LMICs. These findings highlight a critical gap in antimicrobial resistance surveillance and highlight the need for stewardship strategies that effectively engage this provider group.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".