A Competency-Guided Veterinary Curriculum Review Process
Bibliographic record
Abstract
Competencies can guide outcomes assessment in veterinary medical education by providing a core set of specific abilities expected of new veterinary graduates. A competency-guided evaluation of Colorado State University's (CSU) equine veterinary curriculum was undertaken via an alumni survey. Published competencies for equine veterinary graduates were used to develop the survey, which was distributed to large animal alumni from CSU's Doctor of Veterinary Medicine program. The results of the survey indicated areas for improvement, specifically in equine business, surgery, dentistry, and radiology. The desire for more hands-on experiences in their training was repeatedly mentioned by alumni, with the largest discrepancies between didactic knowledge and hands-on skills in the areas of business and equine surgery. Alumni surveys allow graduates to voice their perceived levels of preparation by the veterinary program and should be used to inform curriculum revisions. It is proposed that the definition and utilization of competencies in each phase of a curricular review process (outcomes assessment, curriculum mapping, and curricular modifications), in addition to faculty experience and internal review, is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.115 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".