Veterinary Curricula Today: Curricular Management and Renewal at AAVMC Member Institutions
Bibliographic record
Abstract
Renewing a veterinary curriculum is challenging work and its impact is difficult to measure. Academic leaders are charged with regular review and updating of their curricula, but have few resources available to guide their efforts. Due to the paucity of published veterinary reports, most turn to colleagues at other veterinary schools for insider advice, while a few undertake the task of adapting information from the educational literature to suit the needs of the veterinary profession. In response to this paucity, we proposed a theme issue on curricular renewal and surveyed academic leaders regarding curricular challenges and major renewal efforts underway. We compiled the results of this survey (with respondents from 38 veterinary colleges) as well as publicly available information to create a digest of curricular activities at AAVMC member institutions. This introductory article summarizes the key survey findings, describes the methods used to create the curricular digest, and presents information about key aspects of selected programs. Our overarching research questions were as follows: (1) What was the extent and nature of curricular change at AAVMC-accredited veterinary colleges over the past 5 years? and (2) How are curricula and curricular changes managed at AAVMC accredited veterinary colleges? The appended curricular digests provide selected details of current DVM curricula at participating institutions. Additional articles in this issue report on institutional change efforts in more detail. It is our hope that this issue will help to pave the way for future curricular development, research, and peer-to-peer collaboration.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".