Instruction and Curriculum in Veterinary Medical Education: A 50-Year Perspective
Bibliographic record
Abstract
Our knowledge of veterinary medicine has expanded greatly over the past 50 years. To keep pace with these changes and produce competent professionals ready to meet evolving societal needs, instruction within veterinary medical curricula has undergone a parallel evolution. The curriculum of 1966 has given way, shifting away from lecture-laboratory model with few visual aids to a program of active learning, significant increases in case- or problem-based activities, and applications of technology, including computers, that were unimaginable 50 years ago. Curricula in veterinary colleges no longer keep all students in lockstep or limit clinical experiences to the fourth year, and instead have moved towards core electives with clinical activities provided from year 1. Provided here are examples of change within veterinary medical education that, in the view of the authors, had positive impacts on the evolution of instruction and curriculum. These improvements in both how and what we teach are now being made at a more rapid pace than at any other time in history and are based on the work of many faculty and administrators over the past 50 years.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".