Community pharmacists as antimicrobial resistance stewards: a narrative review on their contributions and challenges in low- and middle-income countries
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global public health crisis that impedes the therapeutic effectiveness of available antimicrobial agents. Due to the high burden of infectious diseases and limited resources, especially trained healthcare professionals, low- and middle-income countries (LMICs) are particularly susceptible to the detrimental effects of AMR. Sometimes, as the first and last point of contact for patients seeking treatment for infections, community pharmacists can play a pivotal role in the stewardship required for AMR. This review aims to highlight the contributions made by community pharmacists in LMICs as AMR stewards. The review considers the challenges from the perspectives of limited resources, inadequate training, a lack of policies and regulations, and issues related to patient behavior. Community pharmacists in LMICs could optimize their advocacy contributions by focusing on One Health AMR stewardship. Transformational and actionable patient and population-centric antimicrobial stewardship (AMS) is feasible with the synergy of policymakers and other healthcare providers in the implementation of AMS policies and programs that support community pharmacists in their efforts to promote rational antimicrobial use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".