Equity in the governance of antimicrobial resistance surveillance: Global experts’ perspectives
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is interwoven with uneven development and increasing socioeconomic demands that shape nature-society relations and place vulnerable groups at increased risk of resistant infections. While equity is integral to AMR governance, its relevance for AMR surveillance systems has not been fully conceptualized. An Urban Political Ecology (UPE) lens premised on equity and sustainable nature-society relations is used to both conceptualize and operationalize equity considerations in the governance of AMR surveillance. Key informant interviews with experts engaged in the Quadripartite Joint Secretariat on AMR, government agencies, academia, and the private sector were conducted to saturation. Exploring equity in AMR surveillance involves clarifying the pathways AMR-related policies and programs impact different sectors of society. Themes identified include global resource distribution, framings and intersectoral collaborations, capacity building, program feasibility, data accessibility, equity-deserving groups, Global North-South divide, sectoral balance, surveillance of root causes, social determinants of health. Greater understanding of these themes could help explain why biomedical approaches alone may not lead to a decline in AMR prevalence in some contexts. Our findings contribute a conceptual understanding of how a UPE lens focused on nature-society relations may assist states in explicitly incorporating equity considerations within One Health AMR surveillance to better address the main drivers of AMR. • Equity considerations must be incorporated in the design of AMR surveillance systems • Urbanization shapes AMR risks and associated vulnerabilities • Social determinants must guide AMR policy and surveillance • Global North-South inequities must be considered in AMR policy and surveillance • AMR Surveillance must target structural root causes
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.
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 | 0.046 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".