Antimicrobial Resistance Threat, One Health Plans, and Administrative Capacity
Bibliographic record
Abstract
ABSTRACT Antimicrobial resistance (AMR) presents a growing global threat that demands coordinated policy action across human, animal, and environmental health sectors. This commentary examines how national AMR action plans, particularly from the United States, Canada, Ethiopia, Tanzania, Saudi Arabia, and Ireland, reflect the principles of administrative capacity and core themes in public administration. Drawing on the framework developed by Joaquin and Greitens (2021), the analysis explores how the dimensions of problem‐solving, management, communication, accountability, and administrative conservatorship are addressed in the design and implementation of these plans. A shared emphasis on environmental surveillance, integrated laboratory networks, public engagement, and evidence‐based decision‐making demonstrates the increasing alignment with a One Health approach. Post‐COVID updates to AMR strategies show growing attention to environmental transmission pathways and a clearer articulation of mission across sectors. However, gaps in accountability, resource allocation, and coordination remain significant, particularly in low‐ and middle‐income countries. A recurring theme is the need to equip policymakers with reliable, actionable information. Although all five elements of administrative capacity are evident across the plans, problem‐solving and management capacity, especially in the operationalization of surveillance and data systems, emerge as the most critical to achieving stated AMR goals. Building these capacities is essential to translating strategic visions into meaningful, measurable outcomes in the global fight against AMR. Building resilient and effective AMR responses will require not only scientific and financial investment but also the administrative infrastructure necessary to support collaboration, transparency, and mission‐driven implementation across agencies and nations.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".