Veterinary Students’ Knowledge and Awareness of Antimicrobial Stewardship Before and After Clinical Rotations
Bibliographic record
Abstract
Given the global threat of antimicrobial resistance, it is imperative that veterinary graduates are effective antimicrobial stewards. Veterinary students learn the principles of antimicrobial stewardship explicitly, through pre-clinical coursework, and implicitly, through the cases they each encounter on clinical rotations. We aimed to understand the influence of pre-clinical versus clinical learning on veterinary students’ knowledge and awareness of antimicrobial concepts to guide efforts to improve instruction in these areas. To assess knowledge acquisition and to explore student perceptions of antimicrobial stewardship, a standardized online survey was administered to Cornell University veterinary students at two timepoints: in August 2020 before clinical rotations ( N = 26 complete responses and N = 24 partial responses) and again in May 2021 after their clinical rotations ( N = 17 complete responses and N = 6 partial responses). Overall and section-specific confidence and knowledge scores were calculated, using pairwise deletion for incomplete responses. Students generally had low confidence in antimicrobial topics and correctly answered only half of knowledge questions correctly; they performed the best on antimicrobial resistance knowledge questions. There were no significant differences in knowledge or confidence after clinical rotations. On average, students had only read one antimicrobial stewardship guideline. Students reported that human health care providers contributed more to antimicrobial resistance than veterinarians. In conclusion, graduating veterinary students at our institution have significant knowledge gaps in critical principles that are essential to become antimicrobial stewards. Explicit instruction in antimicrobial stewardship is necessary in the pre-clinical and clinical coursework, and the practical use of antimicrobial stewardship guidelines should be emphasized.
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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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".