Clinical Veterinary Education: Insights from Faculty and Strategies for Professional Development in Clinical Teaching
Bibliographic record
Abstract
Missing in the recent calls for accountability and assurance of veterinary students' clinical competence are similar calls for competence in clinical teaching. Most clinician educators have no formal training in teaching theory or method. At the University of Tennessee College of Veterinary Medicine (UTCVM), we have initiated multiple strategies to enhance the quality of teaching in our curriculum and in clinical settings. An interview study of veterinary faculty was completed to investigate the strengths and weaknesses of clinical education; findings were used in part to prepare a professional development program in clinical teaching. Centered on principles of effective feedback, the program prepares participants to organize clinical rotation structure and orientation, maximize teaching moments, improve teaching and participation during formal rounds, and provide clearer summative feedback to students at the end of a rotation. The program benefits from being situated within a larger college-wide focus on teaching improvement. We expect the program's audience and scope to continue to expand.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".