Implementing a Simulated Client Program: Bridging the Gap between Theory and Practice
Bibliographic record
Abstract
INTRODUCTION: This paper outlines the design and implementation of an innovative communication skills training program at the Ontario Veterinary College (OVC). Based upon the body of research in human medical education reporting effective results through the use of standardized patients (SPs) for this type of training, an experiential learning laboratory using simulated clients (SCs) and patients was introduced to first-year veterinary students. METHOD: One hundred and four first-year students were assigned to 12 groups of eight or nine students plus a facilitator. Each student interacted with a simulated client and a patient while being observed by peers and a facilitator. The Calgary-Cambridge Observation Guide (CCOG) was used to guide students and facilitators with performance standards and feedback. Assessment strategies were utilized. RESULTS: Implementation of this program required extensive resources, including funding, expertise, facilitator training, time allotment in an already overburdened curriculum, and administrative and faculty support. Preliminary assessment revealed high student and facilitator satisfaction. The potential of this program for student education and assessment was recognized, and it will be expanded in years 2 and 3 of the DVM (Doctor of Veterinary Medicine) curriculum. CONCLUSIONS: Medical educators have created resources, including skills checklists and experiential learning modalities, that are highly applicable to veterinary medical education. Ongoing evaluation of the program is essential to determine whether we are meeting expectations for communication competency in veterinary medicine.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".