Bridging AMR knowledge gaps and improving policy implementation: a perspective on the role of community engagement in Africa
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an escalating global threat, posing a serious challenge to public health and medical and social advancements. Top-down research methods and policy implementation approaches have fallen short in capturing the complexities of AMR transmission and supporting effective policy implementation, including in diverse and resource-constrained settings. This perspective article suggests that community engagement has the potential to improve AMR outcomes by generating more community-relevant, context-specific AMR knowledge, providing novel data and evidence for action. We further note that locally rooted civil society organizations (CSOs) are essential to fostering relevance, acceptability, and effectiveness of AMR interventions, as well as engendering strong commitment to AMR policy development and implementation. We focus on the role that lived experiences and participatory research approaches, such as citizen science, can play in generating locally grounded AMR knowledge, as well as how community engagement can facilitate trust and a sense of ownership surrounding AMR policies among community members. We also note the potential of community engagement to identify and address equity and cross-sectoral coordination challenges and contribute to more equitable and sustainable AMR policy implementation. Drawing on successful initiatives in Nigeria, Zimbabwe, Malawi, and Kenya, and acknowledging the role of advocacy by CSOs, we demonstrate the potential of community-driven approaches to transform AMR responses and human, animal, and environmental health-related outcomes, and note that to be successful, this community engagement must be genuine, meaningful, inclusive, and transparent.
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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 | 0.067 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".