A qualitative scan on the challenges of AMR in Canada and experts’ proposed solutions
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a growing public health threat in Canada. In 2022, rising resistance was reported among key pathogens, yet national coordination remains inconsistent, with strategies varying widely by province and territory. This study explored AMR-related challenges in healthcare and the impact of the COVID-19 pandemic on AMR efforts in Canada. Using a qualitative design, researchers conducted semi-structured interviews with 59 experts from fields such as microbiology, public health, and industry. Participants were identified through environmental scanning and snowball sampling. Thematic analysis of transcripts revealed major barriers to a unified AMR response, including inconsistent surveillance, fragmented stewardship efforts, and decentralized health systems. According to participating AMR experts, COVID-19 disrupted AMR control by diverting resources and potentially increasing resistance but also led to improvements in infection prevention, public awareness, and health infrastructure. Participants emphasized the need for stronger political commitment, improved surveillance, a One Health approach, and better funded, coordinated stewardship programs. With the publication of the Pan-Canadian Action Plan in 2023, the Canadian federal government has announced changes. However, effectively addressing AMR will require a unified, multisectoral strategy that bridges political, health, and societal efforts across jurisdictions.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".