Fostering Integrated Learning and Clinical Professionalism Using Contextualized Simulation in a Small-Group Role-Play
Bibliographic record
Abstract
Teaching and learning in a clinical setting is important in veterinary and medical training but presents many challenges, including providing enough hands-on experience while not putting patients (animal or human) at risk. Some of the issues have been addressed with the introduction of clinical skills laboratories and communication skills training using role play. However, in both instances skills are learned in isolation, whereas the real task requires the integration of many skills including technical competencies, effective communication, decision making, and professionalism. In our study, we trialed "contextualized simulation" by combining role play with a simulator, the haptic cow, in a small-group tutorial, the Simulated Fertility Visit. Students took turns as the veterinarian; they had to establish the cow's history from the farmer (a role player), palpate the simulation, make a diagnosis, and decide on treatment, if appropriate. We included scenarios varying from common cases to challenging situations. The tutorial was introduced in the farm-animal clinical rotation, and feedback was gathered from students by means of a questionnaire. The tutorial was attended by 178 students (98% of that year's students), and 151 questionnaires were returned (85% response rate). Students reported that the tutorial was a positive learning experience and recognized that it presented an opportunity to integrate the skills needed for clinical work. Student feedback suggests that contextualized simulation provides a valuable complement to clinical cases, and we recommend extending this teaching method to other clinical scenarios and species, particularly because it provides a safe environment in which to experience, and learn from, mistakes.
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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 | 0.006 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".