Collaborative Development of a Shared Framework for Competency-Based Veterinary Education
Bibliographic record
Abstract
Competency-based medical education is an educational innovation implemented in health professions worldwide as a means to ensure graduates meet patient and societal needs. The focus on student-centered education and programmatic outcomes offers a series of benefits to learners, institutions and society. However, efforts to establish a shared, comprehensive competency-based framework in veterinary education have lagged. This article reports on the development and outcome of a competency-based veterinary education (CBVE) framework created through multi-institutional collaboration with international input from veterinary educators and veterinary educational leaders. The CBVE Framework is designed to reflect the competencies expected of new graduates from member institutions of the Association of American Veterinary Medical Colleges (AAVMC). The CBVE Framework consists of nine domains of competence and 32 competencies, each supplemented with illustrative sub-competencies to guide veterinary schools in implementing competency-based education in their local context. The nine domains of competence are: clinical reasoning and decision-making; individual animal care and management; animal population care and management; public health; communication; collaboration; professionalism and professional identity; financial and practice management; and scholarship. Developed through diverse input to facilitate broad adoption, the CBVE Framework provides the foundation for competency-based curricula and outcomes assessment in veterinary education internationally. We believe that other groups seeking to design a collective product for broad adoption might find useful the methods used to develop the CBVE Framework, including establishing expertise diversity within a small-to-medium size working group, soliciting progressive input and feedback from stakeholders, and engaging in consensus building and critical reflection throughout the development process.
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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 | 0.112 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".