Virtual VM4 Clinical Rotations: A COVID-19 Pandemic Response at Iowa State University College of Veterinary Medicine
Bibliographic record
Abstract
Policy changes in response to the coronavirus disease 2019 (COVID-19) pandemic at Iowa State University College of Veterinary Medicine (ISU-CVM) included the administrative directive that fourth-year (VM4) clinical rotations immediately transition from in-person to virtual format. This article summarizes the efforts, successes, and challenges experienced by ISU-CVM clinical faculty during this transition. Numerous data sources were reviewed, including college records and announcements, faculty survey results, and student rotation evaluations. Data were explored using quantitative and qualitative methods. Between March and July 2020, 36 faculty from 15 different clinical services invested approximately 5,000 hours in delivering virtual content to 165 VM4 students from ISU-CVM and Caribbean veterinary schools. With departmental, college, and university assistance, faculty effectively used educational technologies (Zoom, Canvas, Echo360) and developed adaptive and innovative methods for virtual content delivery. Virtual VM4 rotations were collectively well received and appreciated by students, and student evaluation scores for virtual rotations were statistically equivalent to or higher than those for the corresponding in-person rotations in the preceding year. Although certain hands-on skills could not be adequately acquired in a virtual environment, students gained theoretical knowledge and case-based problem-solving skills in the online format. Faculty reported satisfaction with their adaptability and resilience in these challenging circumstances. These findings demonstrate that ISU-CVM clinical faculty invested substantial time and effort to transition in-person clinical rotations to virtual format during the early COVID-19 pandemic. This is particularly noteworthy given that many of these same faculty simultaneously served as essential personnel managing clinical cases in the university's teaching hospital.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".