Evaluation of Peer Simulations Utilizing Student-Generated Case Studies with Pre-clinical Veterinary Students
Bibliographic record
Abstract
A novel student-driven model of peer simulations using reverse case studies was developed during the COVID-19 pandemic to provide virtual instruction to fourth-year clinical veterinary students. Focus groups suggested that, while this teaching method could not replace hands-on clinical experience, it could be a valuable tool to clinically prepare students during their pre-clinical curriculum. The primary aim of this study was to determine whether this teaching method enhanced earlier curricular student comfort with clinical reasoning, communication, and peer role play as measured by pre- and post-surveys. A secondary aim was to evaluate clinical reasoning ability using the validated Modified Lasater Clinical Judgment Rubric (MCJR). Eighteen pre-clinical veterinary students participated in a 1-week course where they designed and presented clinical cases and participated through virtual role play as clients, clinicians, and observers. Our results demonstrated that students’ comfort in clinical reasoning and peer role play significantly improved ( p < .001 and p = .003, respectively) after participating in this activity. The role perceived to be the most helpful at developing clinical reasoning and communication skills was clinician, followed by client then observer. Results from the MCJR found significant discrepancies between facilitator scoring and student self- and peer-assessment ( p < .001). Common themes emerged including the benefits of engaging in self-reflection, peer-to-peer learning, experiencing case ownership and autonomy, and practicing communication and clinical reasoning skills. This teaching method provides a valuable alternative to client simulators and suggests having students create a case as a client offers a unique educational opportunity.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".