Review: Governance principles for AMR surveillance: A scoping review and conceptual framework — R0/PR3
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a pressing global health issue with serious implications for health, food security, and livelihoods. Collective action, from local to global, that draws on the One Health (OH) approach to facilitate collaboration between the human, animal, and environmental sectors is required to inform initiatives to mitigate AMR. For AMR surveillance, this involves applying an intersectoral, multistakeholder perspective to guide the co-creation of knowledge and policy around the collection, analysis, and application of surveillance data to detect, monitor, and prevent AMR health threats. Currently, there is little available evidence on how to operationalize a OH approach to support integrated AMR surveillance systems, or on how the governance of such systems facilitates intersectoral action on AMR. We conducted a scoping review of the literature to identify the governance domains most relevant to applying the OH approach to the design and evaluation of AMR surveillance systems. We found that governance is a crucial component of the development of surveillance systems equipped to tackle complex, structural issues such as AMR. The governance domains identified include participation, coordination and collaboration, management, sustainability, accountability and transparency, and equity. These domains are relevant throughout all stages of policy design, implementation, and evaluation of AMR surveillance systems. Equity is both a domain and an essential component of the other domains. All the domains are interdependent and co-constitutive, so that progress in one domain can accelerate progress in another. The conceptual framework presented in this article can inform the design and evaluation of OH AMR governance systems and other complex health challenges that have similar barriers and facilitators to OH governance. The qualitative evaluation questions developed for each domain facilitate assessment of the breadth (the range of actors involved in governance) and depth (how meaningful their engagement is) for each domain relevant to OH governance. Finally, the prioritization of formal, sustainable, and democratic governance of AMR can help to facilitate achievement of the sustainable development goals (SDGs) and promote conservation of the use of antimicrobials for future generations.
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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 | 0.043 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".