Current Methods in Use for Assessing Clinical Competencies: What Works?
Bibliographic record
Abstract
An online survey was used to capture qualitative descriptions of methods used by a veterinary college to assess clinical competencies in its students. Each college was specifically asked about use of the methods detailed in the Toolbox of Assessment Methods developed by the Accreditation Council for Graduate Medical Education and the American Board of Medical Specialties. Additionally, each college was asked to detail the methods used to ensure competency in each of the nine areas specified by the American Veterinary Medical Association Council on Education. Associate deans of academic affairs or their equivalents at veterinary colleges in the United States, the United Kingdom, Canada, and the Caribbean were contacted by e-mail and asked to complete the survey. Responses were obtained from 24 of 32 colleges. The methods most often used were review of students' medical records (16), checklist evaluation of must-learn skills (16), procedural logs (11), multiple-choice skill examinations (11), case simulations using role-playing (7), short-answer skill examinations (7), global rating of live or recorded performance (7), case simulations using computerized case simulations (7), 360-degree evaluation of clinical performance (4), and standardized patient or client examination (3). Additional methods used included medical record portfolio review, paper-and-pencil branching problems, chart-stimulated oral exams, externship mentor evaluation, performance rubrics for clinical rotations, direct observation and query on cases, video evaluation, case correlation tasks, and an employer survey. Non-realistic models were used more often for skill evaluation than realistic models. One college used virtual-reality models for testing.
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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 | 0.121 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".