Veterinarian-client-patient communication patterns used during clinical appointments in companion animal practice
Bibliographic record
Abstract
OBJECTIVE: To identify communication patterns used by veterinarians 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. The Roter interaction analysis system was used to analyze the resulting 300 videotapes, and cluster analysis was used to identify veterinarian communication patterns. RESULTS: 175 (58%) appointments were classified as having a biomedical communication pattern, and 125 (42%) were classified as having a biolifestyle-social communication pattern. None were classified as having a consumerist communication pattern. Twentythree (46%) veterinarians were classified as using a predominantly biomedical communication pattern, 19 (38%) were classified as using a mixed communication pattern, and 8 (16%) were classified as using a predominantly biolifestyle-social communication pattern. Pattern use was related to the type of appointment. Overall, 103 (69%) wellness appointments were classified as biolifestyle-social and 127 (85%) problem appointments were classified as biomedical. Appointments with a biomedical communication pattern (mean, 11.98 minutes) were significantly longer than appointments with a biolifestyle-social communication pattern (10.43 minutes). Median relationship-centered care score (ie, the ratio of client-centered talk to veterinarian-centered talk) was significantly higher during appointments with a biolifestyle-social communication pattern (1.10) than during appointments with a biomedical communication pattern (0.40). CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that veterinarians in companion animal practice use 2 distinct patterns of communication. Communication pattern was associated with duration of visit, type of appointment, and relationship-centeredness. Recognition of these communication patterns has implications for veterinary training and client and patient outcomes.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".