Mainstreaming Alternatives in Veterinary Medical Education: Resource Development and Curricular Reform
Bibliographic record
Abstract
Veterinary medical educators are charged with preparing students to enter practice in veterinary medicine during a four-year, intensive, professional education program. This requires giving students in laboratory training that involves dead, anesthetized, or conscious animals, so that they become proficient in the expected range of veterinary knowledge, skills, and abilities. Undeniably, experience with animals is essential to prepare students for a profession in which animals comprise the total domain. However, the consumptive use of animals for teaching students, especially in laboratories, is increasingly subject to regulatory requirements, while also being scrutinized by animal protection groups, and has become a common focus of contention among veterinary students. Not surprisingly, the use of animals in teaching has sharply declined over the past few decades, as new teaching resources and methods, involving less consumptive use of animals, have been incorporated. This change in veterinary medical education has occurred on such a wide scale, in almost all veterinary schools and colleges, that the educational approach can serve as a model for further developments within the veterinary educational community and, indeed, for animal-related material in secondary schools and undergraduate higher education. This article highlights examples of the leadership provided by veterinary educators in developing alternative teaching resources and methods, while maintaining the high level of proficiency expected from traditional educational approaches.
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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| 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".