Use of Actors or Peers as Simulated Clients in Veterinary Communication Training
Bibliographic record
Abstract
Using simulated clients is an effective teaching method for training and assessing communication skills in veterinary education. The aim of this study is to evaluate the use of actors and peers in communication skills training in veterinary medicine. For this purpose, the subjective perception of the use of actors was assessed in a first study using a paper-based self-evaluation survey. In a second study, different groups of veterinary students who trained their communication skills with actors or peers were compared in an electronic Objective Structured Clinical Examination (eOSCE) assessment with regard to their outcomes of communication proficiency. All participants reported the actors to be helpful and supportive in learning communication skills. Above all, participants highly rated the achieved authenticity when using actors as well as feedback sessions. Regarding the comparison of actors and peers as teaching methods, no significant difference in the performance of veterinary students in an eOSCE was identified. Despite the lack of objective evidence, both methods may be considered valuable and accepted teaching tools. Training with peers gives students an opportunity to learn how to conduct structured history interviews and to understand pet owners' motives at an early stage of undergraduate veterinary training. Change of perspective is considered a positive training element. However, when portraying authentic and standardized emotions and reactions and giving formative feedback based on the pet owners' internal perspectives, actors are beneficial for training advanced veterinary students and graduates in difficult conversation topics.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".