One Health Evaluation of Antimicrobial Use and Resistance Surveillance: A Novel Tool for Evaluating Integrated, One Health Antimicrobial Resistance and Antimicrobial Use Surveillance Programs
Bibliographic record
Abstract
We describe the development, application and utility of our novel, One Health Evaluation of Antimicrobial Use and Resistance Surveillance (OHE-AMURS) tool that we created to evaluate progress toward integrated, One Health surveillance of antimicrobial resistance (AMR) and antimicrobial use (AMU) as a complex system in Canada. We conducted a qualitative inquiry into the current state of policy and programs for integrated AMR/AMU surveillance using explicit and tacit knowledge. To assess the "messy" state of public health surveillance program development, we synthesized recommendations from previous reports by the National Collaborating Centre for Infectious Diseases and the Canadian Council of Chief Veterinary Officers; conducted an environmental scan to find all federal, provincial, and territorial AMR/AMU surveillance programs in Canada; and conducted semi-structured interviews with Canadian subject matter experts. To integrate evidence from these different sources we adapted two published tools to create a new evaluation matrix, deriving 36 components of the ideal integrated AMR/AMU surveillance system. Our two-way matrix tool allowed us to examine seven common, foundational elements of sustainable programs for each component, and assign a stage of development/sustainability ranking for each component according to the matrix definitions. Our adaptable novel tool allowed for granular and repeatable assessment of the many components of a complex surveillance system. The assessment proved robust and exacting to ensure transparency in our methods and results. The matrix allows flexible assignment of program components based on program principles, and stages can be adapted to evaluate any aspect of an AMR/AMU surveillance or other multi-faceted, multi-jurisdictional system. Future refinement should include an assessment of the scope of surveillance components.
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How this classification was reachedexpand
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 | 0.018 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".