The Students for Antimicrobial Stewardship Society: A Novel, Grassroots Educational Approach to Growing a Culture of Antimicrobial Stewardship
Bibliographic record
Abstract
Background. The World Health Organization identified antibiotic resistance (AR) as this century's most critical health issue. Antimicrobial stewardship (AS) education has been shown to reduce AR, but such educational interventions have historically targeted practitioners after their prescribing habits are established. Given medical students' demonstrated interest in AS and the lack of its integration into undergraduate medical education, we sought to strengthen student exposure to AS. Methods. The Students for Antimicrobial Stewardship Society (SASS) was founded by Canadian medical students in 2015 to empower the next generation of health care providers to prescribe antimicrobials judiciously through peer-to-peer education. SASS has become a multidisciplinary, student-led, national organization dedicated to raising awareness of AR and AS, and is the first organization of its kind. It developed a 3-pronged focus: communication, education and policy development, and external relations. SASS has also completed a narrative review of AS educational evaluations, a pre-clerkship curriculum evaluation, and a point- prevalence study of AS training and interest among clerkship students. Results. SASS has support from the Canadian Federation of Medical Students and Ontario Medical Students' Association, and established 8 student chapters across Canada. It was selected as 1 of 15 globally leading initiatives by the International Federation of Medical Students Associations. SASS presented to Health Canada, the Chief Medical Officer, and Public Health Agency of Canada. Furthermore, SASS has worked with pharmacy, dentistry and nursing to create student teams that develop multidisciplinary AS solutions. In its studies, SASS has found a dearth of evaluated educational practices in undergraduate medical education, low to moderate exposure in curricula, and a high level of interest among students for more resources. Conclusion. Through peer-to-peer education, research, advocacy and faculty collaboration, SASS has developed an innovative approach to improving medical student awareness of AS and provides new understanding of current educational practices. SASS is a model for developing grassroots programs for AS education in many disciplines. Disclosures. All authors: No reported disclosures.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| 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".