Evaluation of Student Abilities to Respond to a “Real-World” Question about an Emerging Infectious Disease
Bibliographic record
Abstract
Veterinarians play an important role in educating the public about emerging animal and zoonotic diseases. This article investigates the ability of third-year veterinary students (N=31), from a veterinary school in the USA, to respond to an actual client's question about an emerging disease. In an open-book, take-home examination, students were asked to respond to a nurse's concern that she could bring home influenza from work and infect her macaw. While 75% of the students answered the question correctly, only 51% demonstrated that they understood that this question came from the ongoing publicity about the highly pathogenic H5N1 avian influenza outbreak in Asia, Africa, and Europe. Additional information that would have decreased the client's concern and provided the client with a better understanding of this disease outbreak was lacking in many of the answers. The results of this study suggest that greater emphasis should be applied to exercises requiring veterinary students to research, carefully study, and formulate answers to applied topics that are novel to them.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".