Creating Veterinary Public Health Online Cases by Students for Students
Bibliographic record
Abstract
Online-based processing of case reports is often used and well accepted in veterinary medical education. However, lecturers usually develop cases from their own point of view, without input from students. In order to give students the chance to create online cases for students, an elective course Creative Workshop Case Creation, was held three times between 2017 and 2019 at the Faculty of Veterinary Medicine, Freie Universität Berlin. During this course, students created cases based on animal welfare and epizootics issues through a problem-based blended learning approach. In this approach, students worked on an assigned veterinary public health problem and actively solved it in small groups in class and then used the issue as the basis to create cases for their fellow students. The cases were implemented in interdisciplinary lectures, which are mandatory for every student in semesters six to eight. After taking these classes, fellow students evaluated one of these cases, specifically, on animal welfare and another one on epizootics. Evaluations showed these cases were received well. Moreover, we received excellent feedback from students participating in the elective course, and working with a proactive and motivated group of six students throughout the course was a very productive experience. The course made it possible to create cases that are more accurately tailored to the needs of students. The students' good ideas and preparatory work also saved time in the preparation of cases for lecturers.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.016 |
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".