Use of the Roter interaction analysis system to analyze veterinarian-client-patient communication in companion animal practice
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To identify specific components of veterinarian-client-patient communication during clinical appointments in companion animal practice. DESIGN: Cross-sectional descriptive study. SAMPLE POPULATION: A random sample of 50 companion animal practitioners in southern Ontario and a convenience sample of 300 clients and their pets. PROCEDURE: For each practitioner, 6 clinical appointments (3 wellness appointments and 3 appointments related to a health problem) were videotaped, and the Roter interaction analysis system (RIAS) was used to analyze the resulting 300 videotapes. Statements made during each appointment were classified by means of a communication framework reflecting the 4 essential tasks of the appointment (ie, data gathering, education and counseling, relationship building, and activation and partnership). RESULTS: 57% of the veterinarians contacted (50/87) and 99% of the clients contacted agreed to participate in the study. Mean duration of the appointments was 13 minutes. Typically, veterinarians contributed 62% of the total conversation and clients contributed 38%. Fifty-four percent of the veterinarian interaction was with the client, and 8% was with the pet. Data gathering constituted 9% of the veterinarian-to-client communication and was primarily accomplished through closed-ended questioning; 48% of veterinarian-to-client communication involved client education and counseling, 30% involved relationship building, and 7% involved activation and partnership (the remaining 6% constituted orientation). CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that the RIAS was a reliable method of assessing the structure, process, and content of veterinarian-client-patient communication and that some veterinarians do not use all the tools needed for effective communication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it