Virtual Clinics: A Student-Led, Problem-Based Learning Approach to Supplement Veterinary Clinical Experiences
Bibliographic record
Abstract
The COVID-19 pandemic created an abrupt need for effective remote clinical experiences for senior clinical veterinary students. Subsequently, the authors created virtual clinics. This activity was derived from a problem-based learning (PBL) model wherein students designed clinical cases and participated through virtual role play as clients and clinicians. The purpose of this article is to describe virtual clinics and to report data from focus groups of participating students and faculty facilitators from two institutions regarding the positive and negative aspects of the shift in practice. A few common emerging themes included that case rounds were fun and engaging, students could learn at their own pace, and peer-to-peer learning opportunities had perceived value. Themes are reflected against the pedagogical literature to draw out areas that resonated. Students felt this activity was more engaging than listening to a discussion of a case they had no ownership of, and facilitators agreed that the peer-to-peer interactions added to student engagement. Additionally, students developed deeper knowledge about the underlying disease process and clinical presentation of their case, which required independent and self-directed learning, enabling students to think about a case from a client's perspective. By participating in these activities, students developed skills of classroom-to-clinic transitional value. While virtual clinics should not replace in-person clinical experiences, this activity might be useful to facilitate students' transition from a structured classroom setting to a less-structured clinical experience.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".