Antimicrobial resistance and use in Canada: A federal framework for action
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a serious and growing global public health threat.Modern medical and veterinary practice depends on the widespread availability of effective antimicrobials to prevent and treat infections in humans and animals.Addressing the growing threat of AMR in Canada is a shared responsibility; Antimicrobial Resistance and Use in Canada: A Federal Framework for Action serves as a starting point for a collaborative response.The goal of the Framework is: "To protect Canadians from the health risks related to antimicrobial resistance."It includes three pillars: Surveillance, Stewardship, and Innovation.The Framework identifies concrete Government of Canada actions to reduce the threat and impact of AMR.Equally important, it is a vehicle to engage partners and stakeholders in discussions on efforts that, together, can significantly increase the results of individual actions.Beyond the Government of Canada, provinces and territories, academia, animal and human health professionals, food production stakeholders, animal producer groups and farmers, as well as private industry each hold essential levers to reduce AMR.By continuing to work together, we will collectively achieve greater results in reducing the risks of antimicrobial resistance and protecting the health and safety of all Canadians.
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 | 0.048 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".