A Novel Approach to Simulation-Based Education for Veterinary Medical Communication Training Over Eight Consecutive Pre-Clinical Quarters
Bibliographic record
Abstract
Experiential learning through the use of standardized patients (SPs) is the primary way by which human medical schools teach clinical communication. The profession of veterinary medicine has followed suit in response to new graduates' and their employers' concerns that veterinary interpersonal skills are weak and unsatisfactory. As a result, standardized clients (SCs) are increasingly relied upon as invaluable teaching tools within veterinary curricula to advance relationship-centered care in the context of a clinical scenario. However, there is little to no uniformity in the approach that various colleges of veterinary medicine take when designing simulation-based education (SBE). A further complication is that programs with pre-conceived curricula must now make room for training in clinical communication. Curricular time constraints challenge veterinary colleges to individually decide how best to utilize SCs in what time is available. Because it is a new program, Midwestern University College of Veterinary Medicine (MWU CVM) has had the flexibility and the freedom to prioritize an innovative approach to SBE. The author discusses the SBE that is currently underway at MWU CVM, which incorporates 27 standardized client encounters over eight consecutive pre-clinical quarters. Prior to entering clinical rotations, MWU CVM students are exposed to a variety of simulation formats, species, clients, settings, presenting complaints, and communication tasks. These represent key learning opportunities for students to practice clinical communication, develop self-awareness, and strategize their approach to future clinical experiences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".