Developing and Implementing a Competency-Based Veterinary Medicine Program at the Université de Montréal
Bibliographic record
Abstract
This article outlines the comprehensive reform of the Doctorate in Veterinary Medicine (DVM) program at the Université de Montréal, with a focus on the integration of a competency-based approach within the existing curriculum. The primary purpose of this reform was to enhance student competency and address specific deficiencies in competencies as revealed by annual outcomes assessment surveys. The authors developed a competency framework with seven competencies and specific elements, providing a foundation for the educational redesign. This framework guided the creation of learning-assessment situations (LAS) aimed at promoting active and contextualized learning throughout the program. The competency development and assessment pathway (CDAP) matrix was established to align LAS within the traditional program structure and track student progress. A learning portfolio and a competency certification process were introduced to support student learning and assess competency achievement. The authors discuss change management, including the paradigm shift toward programmatic assessment, and provide insights into the evolution of the program post-implementation. Preliminary outcomes assessment reveals positive changes in how teaching staff and students perceive the program. Despite challenges related to human resource constraints, the authors emphasize the significance of this reform, which aligns with current trends in medical education. This paper underscores the importance of tailoring educational approaches to specific institutional environments while maintaining programmatic rigor and quality assurance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".