Criterion-Referenced Evaluation of Day One Clinical Competencies of Veterinary Students: VOLES–the VMTH (Veterinary Medicine Teaching Hospital) Online Evaluation System
Bibliographic record
Abstract
This article describes an extensive online criterion-referenced evaluation system for the assessment of veterinary students' achievement during their final year's Doctor of Veterinary Medicine (or equivalent) clinical education. Data are reported for the 2001 to 2009 University of California at Davis veterinary graduates, for a total of more than 1,100 students. These criterion-referenced evaluations extensively document the level of clinical skills attained and demonstrated during the individual clinical rotations that comprise the fourth-year curriculum. On average, in each of the 17,500 clinical rotations undertaken during this time period, student performance was assessed in at least 11 separate areas of skills, knowledge, and professional attributes. This provided more than 200,000 criterion-referenced judgments of the individual clinical attributes of graduates over nine years. The system is based on a previously detailed and validated definition of the skills, knowledge, and professional attributes that students should have demonstrated before graduation. The extensive database that this system has provided has established that this system, termed VOLES (VMTH [Veterinary Medicine Teaching Hospital] On-Line Evaluation System), is an effective tool to assess the clinical capabilities of veterinary students and their achievement of the "Day One" skills required for entering clinical practice. These expected proficiencies are balanced according to the differing expectations that each area of veterinary clinical practice demands.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".