“Microbiology Can Be . . . ”: Student Competition to Develop Resources About Infectious Diseases That Improve Health Literacy
Bibliographic record
Abstract
The European Association of Establishments for Veterinary Education emphasizes the importance of communication skills and teamwork for student success in clinical practice. Traditionally, many veterinary curricula lacked standardized formal training in acquiring these essential skills. Effective communication and collaborative teamwork are not only crucial for fulfilling the clinical responsibilities of the veterinary profession but also play a pivotal role in the broader societal context. Veterinarians, in their social role, serve as scientific communicators for the community. This role involves conveying scientific concepts, even complex ones, with a particular emphasis on their significance for public health, reaching a diverse audience. Currently, there is a growing public health necessity to improve health literacy, which refers to the ability to access, understand, appraise, and use information to support healthy choices by society, especially for topics like infectious diseases and vaccination. This became more evident during the global COVID-19 pandemic. This teaching tip describes the development, organization, and broad outcomes of a student competition introduced during a standard veterinary medicine course to design novel resources on microbiology and infectious disease-enhancing health literacy. Three separate events were organized during the academic years 2020-2023. The third-year veterinary medicine students attending the 3-month course on infectious diseases of small animals participated in a student competition aimed at promoting creativity and innovation. Their task was to develop novel resources that delivered informative content to the public concerning microbiology and infectious diseases. Participation was voluntary and students participated in groups of five or six. Overall, 125 students created 22 projects on microbiology and infectious diseases that were able to enhance health literacy. This approach allowed students to engage with the content and convey foundational knowledge to others in an easily accessible way. This skill of communicating with the public using easy-to-understand language is essential for success in the veterinary medicine profession. The resources produced, such as drawings, comics, games, and videos, constitute informative sources. Thus, they were published online on a scientific journal to disseminate knowledge of infectious diseases to a broader audience.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".