A review of antimicrobial stewardship training in medical education
Bibliographic record
Abstract
OBJECTIVES: We reviewed the published literature on antimicrobial stewardship training in undergraduate and postgraduate medical education to determine which interventions have been implemented, the extent to which they have been evaluated, and to understand which are most effective. METHODS: We searched Ovid MEDLINE and EMBASE from inception to December 2016. Four thousand three hundred eighty-five (4385) articles were identified and underwent title and abstract review. Only those articles that addressed antimicrobial stewardship interventions for medical trainees were included in the final review. We employed Kirkpatrick's four levels of evaluation (reaction, learning, behaviour, results) to categorize intervention evaluations. RESULTS: Our review included 48 articles. The types of intervention varied widely amongst studies worldwide. Didactic teaching was used heavily in all settings, while student-specific feedback was used primarily in the postgraduate setting. The high-level evaluation was sparse, with 22.9% reporting a Kirkpatrick Level 3 evaluation; seventeen reported no evaluation. All but one article reported positive results from the intervention. No articles evaluated the impact of an intervention on undergraduate trainees' prescribing behaviour after graduation. CONCLUSIONS: This study enhances our understanding of the extent of antimicrobial stewardship in the context of medical education. While our study demonstrates that medical schools are implementing antimicrobial stewardship interventions, rigorous evaluation of programs to determine whether such efforts are effective is lacking. We encourage more robust evaluation to establish effective, evidence-based approaches to training prescribers in light of the global challenge of antimicrobial resistance.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".