Teaching Tip: Development of Veterinary Anesthesia Simulations for Pre-Clinical Training: Design, Implementation, and Evaluation Based on Student Perspectives
Bibliographic record
Abstract
Anesthesia simulations have been used in pre-clinical medical training for decades to help learners gain confidence and expertise in an operating room environment without danger to a live patient. The authors describe a veterinary anesthesia simulation environment (VASE) with anesthesia scenarios developed to provide a re-creation of a veterinarian's task environment while performing anesthesia. The VASE uses advanced computer technology with simulator inputs provided from standard monitoring equipment in common use during veterinary anesthesia and a commercial canine training mannequin that allows intubation, ventilation, and venous access. The simulation outputs are determined by a script that outlines routine anesthesia scenarios and describes the consequences of students' hands-on actions and interventions during preestablished anesthetic tasks and critical incidents. Patients' monitored physiologic parameters may be changed according to predetermined learner events and students' interventions to provide immediate learner feedback and clinical realism. A total of 96 students from the pre-clinical anesthesia course participated in the simulations and the pre- and post-simulation surveys evaluating students' perspectives. Results of the surveys and comparisons of overall categorical cumulative responses in the pre- and post-simulation surveys indicated improvement in learners' perceived preparedness and confidence as a result of the simulated anesthesia experience, with significant improvement in the strongly agree, moderately agree, and agree categories (p<.05 at a 95% CI). These results suggest that anesthesia simulations in the VASE may complement traditional teaching methods through experiential learning and may help foster classroom-to-clinic transference of knowledge and skills without harm to an animal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".