A Case-Based Learning Approach for Teaching Undergraduate Veterinary Students about Dairy Herd Health Consultancy Issues
Bibliographic record
Abstract
A case-based learning (CBL) format was implemented at the Veterinary School of Nantes, France, for veterinary students in their last year of the curriculum who had chosen to track toward a farm animal career. The focus of the CBL format was learning about dairy herd health consultancy. The goal was to emphasize teamwork among students, introduce professional communications and advisory relationships with clients, and work within the technical and economic limitations of participating farms. These farms volunteered to participate and had identified a problem. The learning objectives included gaining basic knowledge of herd-level diseases and the methods to control these within herds. The program focused on health audits of dairy farms performed by teams of four to five students, culminating in submission of a herd health management action plan specific for the farm visited by each team. The CBL program was comprised of defined learning objectives for each team. The learning process was supervised, from orientation through to validation, by a panel of experts from within the veterinary school and from local industry. Teams submitted written reports that listed recommendations and an action plan for implementation. This report was defended by each team in front of the farmers, their professional partners, and the panel of supervisors. Assessment of the program by students, participating farms, and industry professionals was positive.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".