The Use of Digital Games and Simulators in Veterinary Education: An Overview with Examples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In view of current technological possibilities and the popularity of games, the interest in games for educational purposes is remarkably on the rise. This article outlines the (future) use of (digital) games and simulators in several disciplines, especially in the veterinary curriculum. The different types of game-based learning (GBL)-varying from simple interactive computer board games to more complex virtual simulation strategies-will be discussed as well as the benefits, possibilities, and limitations of the educational use of games. The real breakthrough seems to be a few years away. Technological developments in the future might diminish the limitations and stumbling blocks that currently exist. Consequently, educational games will play a new and increasingly important role in the future veterinary curriculum, providing an attractive and useful way of learning.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| 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 it