Effectiveness of Field Simulation Approach for Problem-Based Learning That Incorporates the One Health Concept
Bibliographic record
Abstract
One Health problem-based learning (PBL) is known as an effective method in teaching zoonotic diseases. However, the classic classroom setting limits real-life exposure for students. Simulation-based learning may improve the learning experience without exposing the students to unnecessary risks. Hence, this study aimed to assess the effectiveness of field simulation PBL compared to a classic classroom setting using a module developed based on the One Health concept by examining the students’ reactions to the learning and by assessing the students’ performance. A quasi-experimental design was adopted in this study. Veterinary and medical undergraduate students participated in both types of PBL settings, and their knowledge and satisfaction were evaluated through a pre- and post-test as well as a feedback survey. The mean satisfaction score of students undergoing field simulation was significantly higher than the mean satisfaction score of students undergoing classic PBL ( p > .05). The respondents from both programs found the field simulation, in comparison to classic PBL, was more effective, and they were more satisfied with the overall learning experience, workloads, and facilitation. The attainment of the cognitive domain was comparable between both PBL groups, which was possibly due to the type of assessment used. In conclusion, field simulation enhanced the students’ positive learning experiences as they exhibited better attitudes toward learning. Future studies on the impact of the simulation on long-term knowledge retention and psychomotor skills are thus warranted.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".