Effects of an 8-Week Dairy Production Medicine Course on Veterinary Student Self-Confidence and Perceptions of Knowledge and Skills Used by Dairy Veterinarians
Bibliographic record
Abstract
The 8-week dairy production medicine course at the National Center of Excellence in Dairy Production Medicine Education for Veterinarians is designed to equip senior veterinary students with the knowledge and skills needed to serve the dairy industry. Course developers identified 59 topics of importance for dairy production medicine veterinarians. Students ( N = 50) were surveyed before and after the course to determine their perceptions of (a) the importance of the 59 topics for their intended positions and (b) their knowledge and skill in those areas. We expected the course to affirm or strengthen perceptions of importance and increase confidence. Students rated 57 of the topics as moderately or very important before the course. Ratings were unchanged (56 topics) or increased (3 topics) after the course. Before the course, students believed they had a lot of knowledge and skill in just one area: animal behavior and handling. At the end of the course, students believed they had a lot of knowledge and skill in 21 areas; confidence ratings were higher for 47 of the 59 topics. Alumni were surveyed 1–2 years after graduation to determine the importance of the 59 topics to their positions, their impressions about how well the course prepared them in those areas, and whether they referred back to course materials. Feedback was used to adjust the course. The topics alumni rated as most important were similar to those students predicted would be most important. Seventy-five percent of alumni used the course website as a resource in 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".