The use of the roter interaction analysis system in assessing veterinary student clinical communication skills during equine wellness examinations in rural Kentucky, USA: A pilot study
Bibliographic record
Abstract
BACKGROUND: Effective clinical communication can aid veterinarians in building good client relationships, increase adherence to recommendations and, ultimately, improve patient health and welfare. However, available information on veterinary communication in the equine context is limited. The objective of this study was to describe the communication of veterinary students in the equine environment who had previous communication training. Additionally, we assessed the suitability of the Roter Interaction Analysis System (RIAS) for the analysis of audio-video recordings of equine wellness consultations. METHODS: Twenty-seven equine wellness consultations performed by second-year Ross University School of Veterinary Medicine students were recorded in rural Kentucky, United States of America. Recordings were submitted to a professional coder who applied the RIAS to the equine context by expanding or adjusting code definitions. RESULTS: A substantial amount of utterances (i.e. segments of speech) were allocated to core communication skills including building rapport (30%), facilitation and client activation (24%) and education and counselling (23%). There was a large variation in utterances used among consultations of the same veterinary student and students; they did not appear anxious or nervous. CONCLUSIONS: Students made use of core communication skills, indicating that experiences from pre-clinical training could be transferred to equine practice. Furthermore, this study demonstrated that the RIAS could be considered for consecutive studies aiming to provide observational data on clinical communication in the equine context.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".