Tracking Veterinary Students’ Acquisition of Communication Skills and Clinical Communication Confidence by Comparing Student Performance in the First and Twenty-Seventh Standardized Client Encounters
Bibliographic record
Abstract
The American Veterinary Medical Association's Council on Education mandates that veterinary students graduate with competence in clinical communication. Communication competence facilitates the successful transition of students into clinical practice by improving veterinary client satisfaction and reducing the risk of litigation. Curricular experimentation with communications training has led to innovative approaches to this content area, including the adoption of standardized patients from human health care education. Although the use of standardized clients (SCs) is time and resource intensive, their use by veterinary educators has allowed students to work through authentic cases in a safe learning environment in which learner goals drive training. Despite the increasing popularity of SC-based communications curricula, outcomes assessment studies that track the resultant student growth are lacking. Ninety students in the inaugural class of 2018 at Midwestern University College of Veterinary Medicine participated in 27 SC encounters over eight consecutive quarters. Students' use of Calgary-Cambridge Guide communication skills was compared in the first and twenty-seventh encounters. On the basis of SC evaluations of student performance, students increased their use of all communication skills in the final encounter compared with baseline. Students were also more likely to take a comprehensive patient history, build rapport, and retain the client. Mean scores for pre- and post-encounter communication confidence, as self-reported by students, also increased. These findings support that a SC-rich curriculum facilitates student acquisition of communication skills and promotes confidence when students approach entry-level clinical tasks such as history taking.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".