Prior Experience, Career Intentions, and Post-Graduate Positions of Veterinary Students Who Participated in an 8-week Dairy Production Medicine Course
Bibliographic record
Abstract
= 50) from seven United States (US) colleges of veterinary medicine took an 8-week dairy production medicine course at the Dairy Center of Excellence in Production Medicine Education for Veterinarians (DCE) between 2012 and 2014. Participants completed a questionnaire before and after the course and 1 to 2 years after graduation. Objectives were to determine the prior academic training and livestock experience of course participants, to compare students' career aspirations before and after taking the course, and to identify factors associated with post-graduate position. Response rates were 58%-96%. Most students had taken undergraduate animal science courses (83%), worked (76%) and/or lived (52%) on a livestock operation, participated in youth livestock activities (63%), worked at a mixed practice (71%), taken production medicine-related elective courses (65%), taken other food animal rotations (91%), and/or done dairy externships (65%) before taking the DCE course. Students who were very likely to pursue a dairy-focused position before taking the course (36%) remained committed after the course, whereas students who were not likely initially (39%) were not further motivated by the course. Students who had worked with a dairy veterinarian were more likely to pursue a dairy-focused position than those who had not. Most course alumni accepted positions in mixed practice, with a ≥ 50% (54%) or < 50% (23%) dairy component, and post-graduate positions were consistent with students' predictions. Students who held an undergraduate degree or had worked for a dairy veterinarian were more likely to accept a dairy-focused practice position than those who did not.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".