Student and Clinical Educator Perceptions of the Impacts of COVID-19 on Final-Year Veterinary Clinical Training in a Distributed Learning Model
Bibliographic record
Abstract
Delivery of the fourth year clinical program at the University of Calgary Veterinary Medicine (UCVM) is facilitated through the Distributed Veterinary Learning Community (DVLC) which has underwent major revisions in response to the COVID-19 pandemic. To determine the perceptions of how COVID-19 impacted fourth-year clinical rotations, students ( n = 24) and DVLC practice rotation coordinators (PRCs, n = 23) completed two questionnaires over a 7-month period. The survey consisted of demographic questions, statements ranked on an agreement scale, and open-ended questions. Two-tailed Wilcoxon signed-rank tests and frequency counts were used to analyze their responses over time. Quantitative analysis revealed that 45% students reported concerns for their mental health, 41% for their physical health, and 26% for inadequate clinical time; and 14% cover communication that heightened over a 7-month period. No trends in responses were noted with PRCs over time. Qualitative thematic analysis of students’ responses showed perceived advantages of lower client-induced performance pressure (22%) and longer rotations allowing for increased case responsibility (22%). PRCs felt fulfillment while teaching (50%), enjoyed longer rotations (50%) and used this opportunity to offer future employment opportunities to students (44%). Additionally, there were concerns regarding inadequate clinical time (41%), decreased ability to practice in-person client communication skills (26%), and difficulties enforcing social distancing protocols (43%). Areas of improvement identified from this study include providing clear communication, continued academic support, and normalizing mental health care. Continued adaptations to an ever-changing pandemic landscape can help mitigate the negative effects of future outbreaks and novel situations.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".