Enhancing Veterinary Student Engagement in Public Health and Epidemiology Coursework through a Client-Focused Risk Communication Assignment
Bibliographic record
Abstract
The ongoing COVID-19 pandemic has highlighted the important role veterinarians play as public health communicators and emphasized the importance of engaging veterinary students in epidemiology and public health curriculum, the majority of whom have a clinical focus and struggle to see their relevance in relation to future career plans. To enhance student engagement, second-year DVM students were asked to create a one-page risk communication handout centered on a zoonotic disease and organized with public health message mapping. Informed by the distribution of students' self-declared career plans at admission to the DVM program, students were asked to choose from a list of zoonotic pathogens previously covered in the DVM curriculum and to select a relevant focus species and expected lay audience member. This assignment was scaffolded with previous infectious disease and communication coursework and provided an opportunity for all students to engage with public health material regardless of prior interest or knowledge. Students chose 13 of 15 zoonotic diseases provided, and their species and audience focuses were distributed across previously stated career focuses, including companion animals, food producing animals, exotic animals, and wildlife. Providing options relevant to diverse student experiences and connecting the assignment to clinical competencies supported student autonomy and engagement in public health content outside clinically focused core classes. Students' successful delivery of constructive peer feedback indicated their engagement with the public health course material, integration of learning from other parts of the curriculum, and perceived relevance of the assignment to their future career focus.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".