One health governance of antimicrobial resistance seen through an Urban Political Ecology lens: a critical interpretive synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it