Training Evidence-Based Veterinary Medicine by Collaborative Development of Critically Appraised Topics
Bibliographic record
Abstract
In current veterinary education, skills such as retrieving, critically appraising, interpreting, and applying the results of published scientific studies are rarely taught. In this study, the authors tested the concept of team-based development of critically appraised topics (CATs) in training students in evidence-based veterinary medicine (EBVM). The 116 participants were in their fifth year and attending the clinical rotation at the Clinic for Animal Reproduction. Students developed 18 CATs of varying quality on topics of their choice. Preparing the CATs in teams stimulated discussion on the topic and the quality of the retrieved papers. Evaluation of the project revealed that more than 90% of the students endorsed training in critical appraisal of information in veterinary education. In addition, more than 90% considered the development of CATs an effective exercise for assessing the quality of scientific literature. A provided literature evaluation form was perceived as a useful tool for systematically summarizing a publication's quality. In conclusion, team-based development of CATs during clinical rotations is highly valuable for training in EBVM. Learning and intrinsic motivation seem to be enhanced by creating a situation similar to veterinary practice because the task is embedded into an authentic clinical problem. This approach to clinical training helps to prepare students to integrate evidence from literature into practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.210 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".