Introducing Clinical Behavioral Medicine to Veterinary Students with Real Clients and Pets: A Required Class Activity and an Optional Workshop
Bibliographic record
Abstract
Addressing behavior problems in clinical practice requires diagnostic expertise as well as excellent client skills in communication, gained by experience. This issue was addressed by introducing clinical behavior to first-year veterinary students. The program was implemented over four successive terms (2017-2019) at St. George's University School of Veterinary Medicine. The clinical practice hour was introduced after a brief first-year clinical behavior course (7 lectures). Students were divided into 6-8 person teams. In a class demonstration with a student and his/her dog having behavior problems, two students served as clinicians; a third student, as a scribe, recorded case details. They discussed signalment, history, presenting problems, and possible treatment approaches for 25 minutes; then, the class divided into the assigned teams to develop their specific treatment plans and write up and submit team case reports. During each term, the student Animal Welfare and Behavior Committee organized an optional behavior workshop (enrollment was 24 veterinary students from years 1 through 3). Participation in the workshop included an introductory session and two clinical sessions. Four dog and/or cat cases were scheduled for each of the two sessions. Six students addressed each case: three students were lead clinicians. Workshop evenings concluded with a discussion of all cases. Students were presented a certificate of completion. Students gained early experience in clinical communication, behavior problems, and case write-ups. The abundance of students' pets with behavior problems made this a context that simplified recruiting real cases, but variations could be adapted as appropriate in other communities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.018 |
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".