Assessing Clinical Competency: Reports from Discussion Groups
Bibliographic record
Abstract
This report describes proposed new models for assessment of eight of the nine clinical competencies the American Veterinary Medical Association Council on Education requires for accreditation. The models were developed by discussion groups at the Association of American Veterinary Medical Colleges' Clinical Competency Symposium. Clinical competencies and proposed models (in parentheses) are described. Competency 1: comprehensive patient diagnosis (neurologic examination on a dog, clinical reasoning skills); Competency 2: comprehensive treatment planning (concept mapping, computerized case studies); Competency 3: anesthesia, pain management (student portfolio); Competency 4: surgery skills (objective structured clinical examination, cased-based examination, "super dog" model); Competency 5: medicine skills (clinical reasoning and case management, skills checklist); Competency 6: emergency and intensive care case management (computerized case study or scenario); Competency 7: health promotion, disease prevention/biosecurity (360 degrees evaluation, case-based computer simulation); Competency 8: client communications and ethical conduct (Web-based evaluation forms, client survey, communicating with stakeholders, telephone conversation, written scenario-based cases). The report also describes faculty recognition for participating in clinical competency assessments.
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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 | 0.075 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".