Global resilience and new strategies needed for antimicrobial stewardship during the COVID‐19 pandemic and beyond
Bibliographic record
Abstract
Resilience is having the ability to respond to adversity proactively and resourcefully. The coronavirus disease 2019 (COVID-19) pandemic's profound impact on antimicrobial stewardship programs (ASP) requires clinicians to call on their own resilience to manage the demands of the pandemic and the disruption of ASP activities. This article provides examples of ASP resilience from pharmacists and physicians from seven countries with different resources and approaches to ASP-The United States, The United Kingdom, Canada, Nigeria, Lebanon, South Africa, and Colombia. The lessons learned pertain to providing ASP clinical services in the context of a global pandemic, developing new ASP paradigms in the face of COVID-19, leveraging technology to extend the reach of ASP, and conducting international collaborative ASP research remotely. This article serves as an example of how resilience and global collaboration is sustaining our ASPs by sharing new "how to" do antimicrobial stewardship practices during the COVID-19 pandemic.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.005 | 0.011 |
| 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".