Defining the Attributes Expected of Graduating Veterinary Medical Students, Part 2: External Evaluation and Outcomes Assessment
Bibliographic record
Abstract
We have previously defined a set of 62 attributes-12 in the area of professional characteristics, 28 addressing knowledge and understanding, and 22 delineating skills-that veterinary students should be expected to have demonstrated by the time of their graduation (Walsh DA, Osburn BI, Christopher MM. Defining the attributes expected of graduating veterinary medical students. J Am Vet Med Assoc 219:1358-1365, 2001). We have used this set of attributes as the basis of an outcomes assessment completed by California practitioners to determine whether graduates from the University of California School of Veterinary Medicine are meeting these expectations. Based upon this assessment, these 62 defined attributes appear to reflect very well practicing veterinarians' views and expectations of DVM graduates. The survey results also indicate that, overall, the recent University of California graduates are meeting these set of expectations. Simultaneously, the outcomes assessment focused attention on several areas, including private practice management, work expectations for successful practice, and surgical capabilities. For each, California practitioners recommended that the definition of the expectation be expanded and that the level of achievement by graduates be improved. Defining a set of attributes expected of veterinary graduates is a key step in obtaining an effective outcomes assessment of a professional educational program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".