Challenges in the distribution of antimicrobial medications in community dispensaries in Accra, Ghana
Bibliographic record
Abstract
INTRODUCTION: The dispensation of medicines in some low- and middle-income countries is often carried out by private vendors operating under constrained conditions. The aim of this study was to understand the challenges reported by employees of dispensaries, specifically, chemical and herbal shops and pharmacies in Accra, Ghana. Our objectives were twofold: (1) to assess challenges faced by medicine vendors related to dispensing antimicrobials (antibiotic and antimalarial medications), and (2) to identify opportunities for improving their stewardship of antimicrobials. METHODS: Data were collected in 79 dispensaries throughout Accra, in 2021, using a survey questionnaire. We used open-ended questions, grounded on an adapted socioecological model of public health, to analyze these data and determine challenges faced by respondents. RESULTS: We identified multiple, interlocking challenges faced by medicine vendors. Many of these relate to challenges of antimicrobial stewardship (following evidence-based practices when dispensing medicines). Overall, medicine vendors frequently reported challenges at the Customer and Community levels. These included strained interactions with customers and the prohibitive costs of medications. The consequences of these challenges reverberated and manifested through all levels of the socioecological model of public health (Entity, Customer, Community, Global). DISCUSSION: The safe and effective distribution of medications was truncated by strained interactions, often related to the cost of medicines and gaps in knowledge. While addressing these challenges requires multifaceted approaches, we identified several areas that, if intervened upon, could unlock the great potential of antimicrobal stewardship. The effective and efficient implementation of key interventions could facilitate efforts spearheaded by medicine vendors and leverage the benefits of their role as health educators and service providers. CONCLUSION: Addressing barriers faced by medicine vendors would provide an opportunity to significantly improve the provision of medications, and ultimately population health. Such efforts will likely expand access to populations who may otherwise be unable to access medications and treatment in formal institutions of care such as hospitals. Our findings also highlight the broad range of care provided by shopkeepers and vendors at dispensaries. These findings suggest that the meaningful engagement of dispensaries as valued conduits of community health is a promising pathway for interventions aiming to improve antimicrobial stewardship.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".