Web-based Documentation of Clinical Skills to Assess the Competency of Veterinary Students
Bibliographic record
Abstract
Kansas State University implemented a Web-based program to assess students' competency to perform technical skills during clinical rotations throughout the fourth year of the veterinary curriculum. The classes of 2009 and 2010 recorded a minimum number of procedures (104 and 103, respectively) from a menu of more than 220 recommended procedures. Procedures were categorized by species (small animal, equine, food animal) and disciplines (imaging, anesthesia, diagnostic medicine/necropsy). Ophthalmology was added as a fourth discipline for the class of 2010. Students recorded procedures into the Web-based system, including information about the patient, procedure performed, supervisor, and a self-assessment of performance. Faculty, staff, and house officers evaluated the procedures electronically by confirming that they witnessed the procedure and providing qualitative and written feedback. The class of 2009 recorded 18,492 procedures (M=171/student) and the class of 2010 recorded 16,935 procedures (M=158/student). Two students from each class (2009 and 2010) did not complete the minimum required skills during clinical rotations and returned to perform procedures immediately before (n=3) or immediately after (n=1) graduation to receive their diploma. The Web-based system captured a large number of assessments of technical competency performed in the clinical setting. The system provided students with formative feedback throughout the clinical year, ensured equitable distribution of procedural opportunities across the student body, and required minimal additional resources.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".