Development of a competency-based medical education curriculum for antimicrobial stewardship
Bibliographic record
Abstract
Background: Antimicrobial stewardship (AS) programs are becoming a critical part of infectious diseases (ID) and medical microbiology training programs. As post-graduate medical education shifts toward competency-based medical education (CBME), the curriculum for AS training requires a similar transition. Our objective was to develop an educational curriculum combining principles of AS and CBME and apply a prospective audit and feedback (PAF) as an educational strategy. Methods: A new competency-based educational curriculum (CBEC) was created which addressed multiple stages along the competence continuum. The Centers for Disease Control and Prevention (CDC) core elements for AS were used to generate Entrustable Professional Activities (EPAs) and milestones for this CBEC. Results: Trainees completed a PAF as an AS educational strategy on all antimicrobial starts in a pediatric hospital (141 beds) over a 1-month rotation. The PAF created 26 audits and addressed all (100%) of the CDC's core elements for inpatient AS programs through seven EPAs and 20 milestones. Conclusions: The PAF allowed for 26 interventions to improve effective antimicrobial use and mapped to multiple EPAs and milestones. Additionally, the PAF utilized all of the CDC's core elements for inpatient AS programs. It is imperative to ensure that educational strategies expose residents to AS interventions that have been shown to decrease antimicrobial usage in various settings. The current manuscript may serve as a model for how a CBEC can be developed, and how AS interventions can be integrated into a CBME program.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".