Development and Evaluation of a High-Fidelity Canine Patient Simulator for Veterinary Clinical Training
Bibliographic record
Abstract
High-fidelity human patient simulators have been used for decades in medical education to provide opportunities for students to practice technical skills, diagnostic and therapeutic planning, and communication skills in a safe environment. A high-fidelity canine patient simulator (CPS) was developed using components from a human patient simulator and a low-fidelity foam core canine mannequin. Ninety-six veterinary students participated in cardiopulmonary arrest scenarios in groups of three to five students. Afterwards, participants were asked to complete an anonymous online survey describing their experiences. A total of 70 students (73%) completed the survey. All of the students (100%) felt that the simulator session expanded their cardiopulmonary resuscitation (CPR) knowledge base, and 97% responded that their skills and abilities had improved. Students also expressed positive opinions about the CPS, with 89% agreeing or strongly agreeing that the CPS was realistic and 73% agreeing or strongly agreeing that the scenarios generated emotions similar to real clinical situations. Most participants (98.5%) agreed or strongly agreed that the simulator was an engaging learning experience. Students commonly commented that the simulations allowed them to practice communication and teamwork skills and were more effective than paper-based, problem-oriented learning opportunities and lecture. Students also commented that they wanted more opportunities to participate in simulation exercises. These results suggest that high-fidelity veterinary simulation is an engaging educational methodology that addresses some limitations of other forms of problem-based learning. More studies are needed to quantitatively determine the effectiveness of this novel veterinary educational technology in comparison with more traditional approaches.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".