Beyond NAVMEC: Competency-Based Veterinary Education and Assessment of the Professional Competencies
Bibliographic record
Abstract
The implementation of competency-based curricula within the health sciences has been an important paradigm shift over the past 30 years. As a result, one of the five strategic goals recommended by the North American Veterinary Medical Education Consortium (NAVMEC) report was to graduate career-ready veterinarians who are proficient in, and have the confidence to use, an agreed-upon set of core competencies. Of the nine competencies identified as essential for veterinary graduates, seven could be classified as professional or non-technical competencies: communication; collaboration; management (self, team, system); lifelong learning, scholarship, value of research; leadership; diversity and multicultural awareness; and adaptation to changing environments. Traditionally, the professional competencies have received less attention in veterinary curricula and their assessment is often sporadic or inconsistent. In contrast, the same or similar competencies are being increasingly recognized in other health professions as essential skills and abilities, and their assessment is being undertaken with enhanced scrutiny and critical appraisal. Several challenges have been associated with the assessment of professional competencies, including agreement as to their definition and therefore their evaluation, the fact that they are frequently complex and require multiple integrative assessments, and the ability and/or desire of faculty to teach and assess these competencies. To provide an improved context for assessment of the seven professional competencies identified in the NAVMEC report, this article describes a broad framework for their evaluation as well as specific examples of how these or similar competencies are currently being measured in medical and veterinary curricula.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".