Prescriber-led practice changes that can bolster antimicrobial stewardship in community health care settings
Bibliographic record
Abstract
Stabilizing the emerging resistance of antibiotics depends on our ability to practise appropriate antimicrobial stewardship (AMS). Over 90% of antibiotics dispensed for human use are prescribed in community health care settings rather than in hospitals, with the main prescribers being family physicians, dentists, pharmacists and nurse practitioners working across a broad range of private offices, family health teams, urgent care clinics, emergency departments and long-term care homes. To improve the reach of AMS in community health care settings, the Public Health Agency of Canada partnered with Choosing Wisely Canada in 2017 to develop a focused campaign titled Using Antibiotics Wisely. This campaign is led by the prescribers of antibiotics themselves, who work in community health care settings and are better equipped to identify the specific changes that would support more appropriate use of antibiotics. This article describes these practice changes, the strengths and challenges of Using Antibiotics Wisely and future opportunities to further advance AMS across community health care settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".