Co-constructive Veterinary Simulation: A Novel Approach to Enhancing Clinical Communication and Reflection Skills
Bibliographic record
Abstract
Interpersonal communication is critical in training, licensing, and post-graduate maintenance of certification in veterinary medicine. Simulation has a vital role in advancing these skills, but even sophisticated simulation models have pedagogic limitations. Specifically, with learning goals and case scenarios designed by instructors, interaction with simulated participants (SPs) can become performative or circumscribed to evaluative assessments. This article describes co-constructive veterinary simulation (CCVS), an adaptation of a novel approach to participatory simulation that centers on learner-driven goals and individually tailored scenarios. CCVS involves a first phase of scriptwriting, in which a learner collaborates with a facilitator and a professional actor in developing a client-patient case scenario. In a second phase, fellow learners have a blinded interaction with the SP-in-role, unaware of the underlying clinical situation. In the final part, all learners come together for a debriefing session centered on reflective practice. The authors provide guidelines for learners to gain maximal benefit from their participation in CCVS sessions and describe thematic possibilities to incorporate into the model, with specific case examples drawn from routine veterinary practice. Finally, the authors outline challenges and future directions toward implementing CCVS in veterinary medical education toward the ultimate goal of professional growth and co-evolution as veterinary practitioners.
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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 | 0.005 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".