A framework for improved one health governance and policy making for antimicrobial use
Bibliographic record
Abstract
There is a need to develop an evaluation framework to identify intervention priorities to reduce antimicrobial use (AMU) across clinical, agricultural and environmental settings. Antimicrobial resistance (AMR) can be conceptualised and therefore potentially managed in the same way as an environmental pollution problem. That is, over-use of antimicrobial medicines as inputs to human and animal health leads to unintended leakage of resistance genes that further combine with natural or intrinsic resistance in the environment. The diffuse nature of this leakage means that the private use decision is typically neither cognisant, nor made responsible for the wider social cost, which is the depletion of wider antibiotic effectiveness, a common pool resource or public good. To address this so-called market failure, some authors have suggested a potential to learn from similar management challenges encountered in the sphere of global climate change, specifically, capping use of medically important drugs analogous to limits set on greenhouse gas emissions. Drawing on experience of the economics of greenhouse gas mitigation, this paper explores a potential framework to develop AMU budgets based on a systematic comparative appraisal of the technical, economic, behavioural and policy feasibility of AMU reduction interventions across the One Health domains. The suggested framework responds to a call for global efforts to develop multi-dimensional metrics and a transparent focus to motivate research and policy, and ultimately to inform national and global AMR governance.
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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 | 0.061 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".