Development and Validation of an Escape Game for Food Safety Education: Gamifying the Management of a Food-Borne Health Alert
Bibliographic record
Abstract
Veterinary training programs in Europe must equip future professionals with essential knowledge and skills in food hygiene and technology to assess, promote, and monitor food safety, as outlined by the World Organization for Animal Health Directive 2005/36/EC, and the European Association of Establishments for Veterinary Education standards. In France, veterinary education follows a competency-based framework that includes investigating food chain contamination and participating in multidisciplinary crisis management. To address the challenge of teaching practical public health skills, a food safety escape game was developed at the National Veterinary School of Toulouse. This teaching sequence simulates the management of a food-borne outbreak, using gamification to engage students in problem-solving through a realistic scenario. This study details the game's development, implementation, and efficacy evaluation. Student progress was assessed using a pre-test and post-test survey, and perceptions were gathered through qualitative feedback. Results showed significant improvements in students' understanding of food-borne outbreak management and increased engagement with the subject. A debriefing session following the game further enhanced knowledge retention and reflection. The findings highlight the effectiveness of gamification and small-group problem-solving exercises, such as serious games, in veterinary public health education. In particular, the results of student questionnaires suggest that teaching health crisis management through an escape game linked to a real-life situation, along with the game's constraints (e.g., competition between groups, time limits), is highly valued and can improve the quality of teaching. This study provides valuable insights for educators aiming to enhance student satisfaction and learning outcomes. Furthermore, it demonstrates that escape games offer an innovative approach to teaching complex public health subjects, helping students grasp key concepts in an interactive way. Future efforts will focus on expanding the game's scope, adjusting difficulty levels, and refining mechanics to better meet the diverse needs of veterinary students and professionals.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".