Use of an Escape Room Experience in Emergency Veterinary Medicine Education
Bibliographic record
Abstract
Escape room experiences have been used as an educational tool in several health professions, and to the authors’ knowledge, it is not yet documented in veterinary medicine. They are an example of gamification in a simulated environment where course objectives guide puzzle development. Veterinary emergency medicine can be very stressful as veterinarians often have to make quick decisions. The element of a time limit adds stress to the experience as learners must complete the puzzles within a specified time to successfully escape the room. This article describes the development and delivery of an escape room experience in emergency veterinary medicine to third year students at the Western College of Veterinary Medicine at the University of Saskatchewan. In a survey following the experience, learners indicated they enjoyed participating in the learning activity. They felt it encouraged the use of communication, collaboration, and leadership skills which have been identified as core competencies in veterinary education. While on average learners would disagree with the experience being stressful, several commented that it was a “good stress”. This indicates that the experience may have been considered challenging to learners, which in simulation, supports a positive way to achieving learning objectives that may not overstress participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".