How is an international public health threat advanced in Canada? The case of antimicrobial resistance
Bibliographic record
Abstract
On September 21, 2016, the United Nations General Assembly held a high-level meeting on antimicrobial resistance (AMR). Participating political leaders committed to coordinate action across the human and animal health, agriculture and environmental sectors and to work at national, regional and international levels with the public sector, private sector, civil society and all other relevant actors, including the public. The objective of this article is to outline how the Public Health Agency of Canada (PHAC) has been working to address AMR in Canada. PHAC has used a One Health approach and has been working at the federal level with other government departments and nationally with the provinces, territories, professional organizations and other key players to address AMR. To date, the federal response has focused on surveillance, stewardship and innovation across multiple sectors, including human health, animal health, regulatory actions and research. PHAC is currently working with the provinces and territories as well as key experts in the field to develop a pan-Canadian AMR Framework and subsequent action plan that will outline best practices and approaches to AMR across human and animal health. The Framework will build on previous work done by PHAC and the federal/provincial/territorial Pan-Canadian Public Health Network Council, and recognizes the research expertise in Canada, the need to ensure actions are based on evidence, and to combat AMR through infection prevention and control. The three articles in this issue are examples of the foundational work that has been done federally by PHAC, in developing the Canadian AMR Surveillance System (CARSS), and nationally, through task groups of the Public Health Network Council, in identifying where to strengthen human surveillance of AMR and best practices for stewardship in the human health care system. While we remain in an early stage of national, coordinated AMR action, momentum is building to ensure Canada can respond to this global health threat with a One Health approach involving multiple sectors at local, national and international levels that are all well-aligned with the World Health Organization Global Action Plan.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.077 | 0.045 |
| Scholarly communication | 0.033 | 0.013 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.012 | 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".