How can we tackle the overuse of antibiotics in low- and middle-income countries?
Bibliographic record
Abstract
INTRODUCTION: Antibiotic overuse is a pressing global health concern, particularly in low- and middle-income countries (LMICs) where there is limited access to quality healthcare and insufficient regulation of antibiotic dispensation. This perspective piece highlights the challenges of antibiotic overuse in LMICs and provides insights into potential solutions to address this issue. AREAS COVERED: This perspective explores key factors contributing to antibiotic overuse in LMICs, encompassing weak healthcare infrastructure, limited access to quality services, and deficiencies in diagnostic capabilities. It discusses regulatory frameworks to curb non-prescription sales, the role of accessible point-of-care diagnostic tools, challenges in implementing effective stewardship programs, the expanded use of vaccines, and the importance of health systems, hygiene, and sanitation. EXPERT OPINION: In this article, we emphasize the need for a comprehensive approach involving collaboration among healthcare professionals, policymakers, researchers, and educators. We underscore the importance of improving healthcare infrastructure, enhancing access to quality services, and strengthening diagnostic capabilities. The article also highlights the significance of education and awareness in promoting responsible antibiotic use, the role of regulatory measures, the expanded utilization of vaccines, and the need for international collaboration to address the challenges of antibiotic overuse in LMICs.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".