Mapping Disciplinary Competencies and Learning Outcomes to the Competency-Based Veterinary Education Framework Using Veterinary Pharmacology as an Example
Bibliographic record
Abstract
The competency-based veterinary education (CBVE) framework describes essential domains of competence and related abilities for veterinary graduates. Translating these outcomes into daily teaching is a challenge, particularly regarding the underpinning basic and clinical science knowledge. In this article, we identified a lack of specific reference to the selection and use of drugs within the CBVE framework; this requires pharmacological knowledge and pharmacology-specific competencies. To fill the gap and provide guidance to veterinary pharmacology educators, we first identified competencies within the CBVE framework relevant to the field of veterinary pharmacology. We then mapped the Day One Competencies in veterinary pharmacology published by Werners and Fajt in 2021 to the pharmacology-relevant CBVE competencies. This exercise led to identifying gaps, redundancies, and a lack of reference to clinical practice within the Day One Competencies in veterinary pharmacology, as well as gaps and ambiguous wording within the CBVE framework. Further research is necessary to update the Day One Competencies in veterinary pharmacology, align basic and clinical pharmacology concepts and skills with the CBVE framework, embed pharmacology-specific competencies into teaching, and identify progression milestones that guide students toward safe prescribing and the appropriate and effective use of drugs.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".