Teaching Veterinary Anesthesia: A Survey-Based Evaluation of Two High-Fidelity Models and Live-Animal Experience for Undergraduate Veterinary Students
Bibliographic record
Abstract
In veterinary medical education, reduction, replacement, and refinement (the three Rs) must be considered. Three clinical skills in anesthesia were identified as challenging to students: endotracheal intubation, intravenous catheterization, and drug dose calculations. The aims of this project were to evaluate students' perception of their level of confidence in performing these three clinical skills in veterinary anesthesia, to document the extent of students' previous experience in performing these three tasks, and to describe students' emotional states during this training. Veterinary students completed a series of four surveys over the period of their pre-clinical training to evaluate the usefulness of high-fidelity models for skill acquisition in endotracheal intubation and intravenous catheterization. In addition, practice and ongoing assessment in drug dose calculations were performed. The curriculum during this period of training progressed from lectures and non-animal training, to anesthesia of pigs undergoing surgery from which they did not recover, and finally to anesthesia of dogs and cats in a neutering clinic. The level of confidence for each of the three clinical skills increased over the study period. For each skill, the number of students with no confidence decreased to zero and the proportion of students with higher levels of confidence increased. The high-fidelity models for endotracheal intubation and intravenous catheterization used to complement the live-animal teaching were considered a useful adjunct to the teaching of clinical skills in veterinary anesthesia. With practice, students became more confident performing drug dose calculations.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".