Development and Evaluation of a Virtual Slaughterhouse Simulator for Training and Educating Veterinary Students
Bibliographic record
Abstract
Veterinary surgeons working on farms and food-processing establishments play a fundamental role in safeguarding both public health and the welfare of animals under their care. An essential part of veterinary public health (VPH) undergraduate training in the UK involves students undertaking placements within abattoirs, a practice that remains vital to the educational experience of future veterinary professionals. However, several issues have adversely affected the ability of students to gain such extramural placements. For this reason, the Virtual Slaughterhouse Simulator (VSS) was developed to strengthen and enhance undergraduate VPH teaching at the Royal (Dick) School of Veterinary Studies, enabling students to explore a realistic abattoir work environment with embedded educational activities. The aim of this research project was to evaluate the VSS as a teaching and learning tool for training and educating veterinary students. Ninety-eight final-year veterinary students engaged with the prototype VSS, followed by assessment of their knowledge and behavior when faced with a "real-life" abattoir situation. Further evaluation of their experiences with the VSS was carried out using questionnaires and focus groups. The results of this investigation show that there is the potential for the VSS to enhance the student learning experience in basic abattoir procedures. This innovative tool provides a visually based learning resource that can support traditional lectures and practical classes and can also be used to stimulate interactive problem-solving activities embedded in the relevant context.
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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 | 0.004 | 0.010 |
| 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 | 0.002 |
| 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".