The Impact of the COVID-19 Pandemic on Veterinary Clinical and Professional Skills Teaching Delivery and Assessment Format
Bibliographic record
Abstract
The limitations posed by the COVID-19 pandemic have been particularly challenging for courses teaching clinical and professional skills. We sought to identify how the COVID-19 pandemic has impacted the delivery of veterinary clinical and professional skills courses, including modifications to teaching and assessment, and to establish educators' perceptions of the efficacy of selected delivery methods. A branching survey was deployed to 35 veterinary schools in North America in March and April 2021. The survey collected data about curriculum and assessment in spring 2020, fall 2020, and spring 2021. Educators at 16 veterinary schools completed the survey (response rate: 46%). Educators quickly adapted curriculum to meet the requirements of their institutions and governments. Early in the pandemic (spring 2020), curriculum was delayed, delivered remotely, or canceled. Assessment methods frequently included virtual objective structured clinical examinations (OSCEs) and video-recorded skills assessments. Later in the pandemic (fall 2020, spring 2021), in-person clinical skills sessions resumed at many schools, often in smaller groups. Professional skills instruction typically remained virtual, as benefits were noted. Assessment methods began to normalize with in-person OSCEs resuming with precautions, though some schools maintained virtual assessments. Educators noted some advantages to instructional methods used during COVID, including smaller group sizes, better prepared students, better use of in-person lab time, more focus on essential course components, provision of models for at-home practice, and additional educators' remote involvement. Following the pandemic, educators should consider retaining some of these changes while pursuing further advancements, including improving virtual platforms and relevant technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".