Implementing Competency-Based Veterinary Education: A Survey of AAVMC Member Institutions on Opportunities, Challenges, and Strategies for Success
Bibliographic record
Abstract
Competency-based education is increasingly being adopted across the health professions. A model for competency-based education in veterinary medicine was recently developed by a working group of the American Association of Veterinary Medical Colleges (AAVMC) and is being used in institutions worldwide. The purpose of this study was to gather information on progress in and barriers to implementing competency-based education (including use of the AAVMC competency-based veterinary education [CBVE] Model) by AAVMC member schools to inform the development of strategies to support institutions in successful implementation of the CBVE Model. A cross-sectional survey was developed and distributed to AAVMC member institutions via an AAVMC web-based communication platform. Thirty-four of 55 AAVMC member institutions responded to the survey (62% response rate). Twenty schools reported using a competency-based education framework. Eleven of these institutions had implemented the AAVMC CBVE Framework, with an additional 12 institutions anticipating implementing it over the next 3 years. Timing, resources, and change management were the most commonly reported challenges to implementation. Suggestions for development of training resources included translation of milestones to pre-clinical courses, development of assessments, guidance on making progress decisions, illustrative overviews of specific components of the CBVE Model (e.g., the AAVMC CBVE Framework, EPAs, entrustment scales, milestones), and curriculum mapping. This study assesses progress in implementing the CBVE Model in AAVMC member schools and aids in identifying key challenges and resources to support faculty and institutions in the successful adoption and implementation of this educational model.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".