Integrated surveillance of antimicrobial resistance and antimicrobial use: Evaluation of the status in Canada (2014–2019)
Bibliographic record
Abstract
OBJECTIVE: Integrated surveillance of antimicrobial resistance (AMR) and antimicrobial use (AMU) across One Health sectors is critically important for effective, evidence-based policy, stewardship, and control of AMR. Our objective was to evaluate progress towards achieving comprehensive, integrated AMR/AMU surveillance in Canada. MATERIALS AND METHODS: Based on an environmental scan, interviews of subject matter experts, and reports from the 2014 National Collaborating Centre for Infectious Diseases and the 2016 Canadian Council of Chief Veterinary Officers, we identified 8 core surveillance requirements and their specific components; the latter were assessed using a 2-way classification matrix, with 7 common elements ranked according to development stage. RESULTS: Components that mapped to requirements of a comprehensive, fully integrated AMR/AMU surveillance system were mostly in the lowest stages of development (Exploration or Program Adoption). However, both the establishment of the Canadian AMR Surveillance System integrated reporting and expansion of existing components under the Canadian Nosocomial Infection Surveillance Program and the Canadian Integrated Program for AMR Surveillance are improvements. Regardless, obvious gaps in Canadian AMR/AMU surveillance prevent this from being a comprehensive and integrated One Health program. CONCLUSION: Action is needed in 3 crucial areas: i) development of a complete, integrated AMR/AMU surveillance program, based on current success; ii) changes in Federal/Provincial/Territorial policies to require standardized AMR/AMU reporting; and iii) more resources for AMR/AMU surveillance (dedicated persons, funding, and enabling structures and policy). There is an urgent need for prioritization by Federal/Provincial/Territorial governments to address governance, leadership, and funding to create surveillance systems that inform stewardship and policy.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".