One health governance of antimicrobial resistance seen through an Urban Political Ecology lens: a critical interpretive synthesis
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a threat to animal and ecosystem health, agriculture, water, and sanitation systems, posing risks not only to human health, but also to society and the systems upon which it depends. Global health governance draws on the One Health (OH) approach to combat AMR. However, the effective implementation of these approaches faces several constraints, including governance and implementation challenges arising from the interconnected nature of AMR with other global health threats, as well as local and structural socioecological factors that affect policy outcomes, that are often overlooked in governance approaches. This article aims to clarify how scientific literature has situated OH-AMR governance responses in relation to six socioecological dimensions: global health threats, broader concerns, governance frameworks, socioeconomic factors, health equity, and environmental justice. Informed by an Urban Political Ecology (UPE) lens and guided by the Critical Interpretive Synthesis (CIS) methodology of Dixon-Woods et al., our critical interpretive synthesis identified 18 articles situating OH-AMR arrangements within these socioecological dimensions. The role of global governance frameworks in shaping state governance arrangements has rarely been the object of analysis in the selected studies. The synthesis highlights the connections between urbanization, AMR risks, global health threats, and broader ecological challenges, calling for a reassessment of current global and state governance approaches. The study also offers a case for the adoption of a UPE lens to address AMR and related global health challenges.
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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 | 0.057 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".