Survey of Instructors Teaching about Antimicrobial Resistance in the Veterinary Professional Curriculum in the United States
Bibliographic record
Abstract
The objective of this study was to ascertain current teaching methods for antimicrobial resistance (AMR) in veterinary professional curricula and to find out what veterinary instructors consider to be prioritized subtopics related to AMR. The sampling frame was instructors in veterinary professional programs at US colleges of veterinary medicine who provide instruction about antibiotics or AMR in the disciplines of microbiology, pharmacology, public health, epidemiology, internal medicine, surgery, or related subjects. Identified instructors were invited to participate in an online survey of current teaching methods related to subtopics of AMR. From 1,207 invitations, 306 completed surveys were available for analysis (25% response rate) with the largest number of respondents stating their contact hours about antibiotics occur in the discipline of "medicine-food animal." The median contact time suggested for AMR in the core veterinary curriculum was 3-5 hours, and for antibiotics in general, 16-20 hours. Subtopics of AMR were prioritized based on respondents' indication that they use or would use various teaching tools. The most common teaching tool for all topics was projected text (i.e., slides or PowerPoint slides) and the least common were video clips, non-course Web sites, online modules, and laboratory experiments. Recommendations for identifying the priorities of AMR content coverage and learning outcomes are made.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".