Teaching Outbreak Investigations with an Interactive Blended Learning Approach
Bibliographic record
Abstract
Public health is a central but often neglected component of veterinary education. German veterinary public health (VPH) education includes substantial theory-focused lectures, but practical case studies are often missing. To change this, we combined the advantages of case-based teaching and blended learning to teach these topics in a more practical and interactive way. Blended learning describes the combination of online and classroom-based teaching. With it, we created an interdisciplinary module for outbreak investigations and zoonoses, based on the epidemiology, food safety, and microbiology disciplines. We implemented this module within the veterinary curriculum of the seventh semester (in the clinical phase of the studies). In this study, we investigated the acceptance of this interdisciplinary approach and established a framework for the creation of interactive outbreak investigation cases that can serve as a basis for further cases. Over a period of 3 years, we created three interactive online cases and one interactive in-class case and observed the student-reported evaluation of the blended learning concept and self-assessed learning outcomes. Results show that 80% (75-89) of students evaluated the chosen combination of case-based and blended learning for interdisciplinary teaching positively and therefore accepted it well. Additionally, 76% (70-98) of students evaluated their self-assessed learning outcomes positively. Our results suggest that teaching VPH through interdisciplinary cases in a blended learning approach can increase the quality of teaching VPH topics. Moreover, it provides a framework to incorporate realistic interdisciplinary VPH cases into the curriculum.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".