Knowledge Gaps in the Understanding of Antimicrobial Resistance in Canada
Bibliographic record
Abstract
Current limitations in the understanding and control of antimicrobial resistance (AMR) in Canada are described through a comprehensive review focusing on: (1) treatment optimization; (2) surveillance of antimicrobial use and AMR; and (3) prevention of transmission of AMR. Without addressing gaps in identified areas, sustained progress in AMR mitigation is unlikely. Expert opinions and perspectives contributed to prioritizing identified gaps. Using Canada as an example, this review emphasizes the importance and necessity of a One Health approach for understanding and mitigating AMR. Specifically, antimicrobial use in human, animal, crop, and environmental sectors cannot be regarded as independent; therefore, a One Health approach is needed in AMR research and understanding, current surveillance efforts, and policy. Discussions regarding addressing described knowledge gaps are separated into four categories: (1) further research; (2) increased capacity/resources; (3) increased prescriber/end-user knowledge; and (4) policy development/enforcement. This review highlights the research and increased capacity and resources to generate new knowledge and implement recommendations needed to address all identified gaps, including economic, social, and environmental considerations. More prescriber/end-user knowledge and policy development/enforcement are needed, but must be informed by realistic recommendations, with input from all relevant stakeholders. For most knowledge gaps, important next steps are uncertain. In conclusion, identified knowledge gaps underlined the need for AMR policy decisions to be considered in a One Health framework, while highlighting critical needs to achieve realistic and meaningful progress.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".