Bridging the Gap Between Veterinary Student Interest and Professional Demand for Poultry-Specialized Veterinarians: A French Experience
Bibliographic record
Abstract
Recent crises concerning poultry production revealed a relative deficit in the availability of veterinary competencies to manage some acute public health and animal welfare concerns. Veterinary education might be critically questioned about this deficit. The authors present the experience of the education program on poultry production medicine at the Veterinary College of Nantes in France over a 10-year period. First, the program consists of integrative teaching focused on a holistic multidisciplinary approach to this professional field on a compulsory basis. Evaluation of the course by the students through a questionnaire (N=1,032) showed a large favorable consensus. Second, the completion of an elective program targeting profession-specific competencies may allow the student to challenge his or her choice of this professional orientation in the undergraduate curriculum. According to the importance they want to give to poultry, and concurrently to other species, students have the possibility of building a curriculum that is either partly or fully devoted to poultry production medicine: a 6-month thesis, 2-10 weeks of professional training, 2 weeks in the field to solve a poultry flock health problem, and 2-4 weeks of specialized courses in poultry production medicine. To round off this curriculum, the national post-graduate program in poultry production medicine is highlighted, as well as its links with the residency program of the European College of Poultry Veterinary Science.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".