Bridging the gaps in the global governance of antimicrobial resistance: the UN sustainable development goals and global health security agenda
Bibliographic record
Abstract
Background : This paper examines the suitability of extant governance frameworks at an international level for addressing antimicrobial resistance (AMR), which is a creeping crisis for global health security. Methods : Our study begins by evaluating the place of antimicrobial resistance (AMR) within United Nations (UN) Sustainable Development Goals (SDG) targets and indicators. This is followed by a discussion of the global health security agenda (GHSA). We examine how AMR needs to be taken more seriously within global policy frameworks based on adopting a One Health approach. The research is supported by a systematic analysis of the national action plans for addressing AMR published by the World Health Organisation (WHO). Results : We determine that political leaders need to do more to promote the problem of AMR and that global health institutions need to invest more energy in thinking about how AMR is governed as part of an already busy global health security agenda. This includes building capacities within health systems, embedding evaluation processes, and enhancing public service leadership within this area. Conclusions : Our review of global policy frameworks and the national plans for AMR highlight the patchy coverage of AMR strategies globally and nationally. This article represents a springboard for future research including whether and to what extent a One Health approach to AMR in the environment has been implemented in practice within national health and environmental systems.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".