Veterinarian-client-patient communication during wellness appointments versus appointments related to a health problem in companion animal practice
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical interview process, content of the medical dialog, and emotional tone of the veterinarian-client-patient interaction during wellness appointments and appointments related to a health problem 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 problem appointments) were videotaped. The Roter interaction analysis system was used to analyze the resulting 300 videotapes. RESULTS: Wellness appointments were characterized by a broad discussion of topics, with 50% of data-gathering statements and 27% of client education statements related to the pet's lifestyle activities and social interactions. Wellness appointments included twice as much verbal interaction with the pet as did problem appointments, and the emotional atmosphere of wellness appointments was generally relaxed. There were more social talk, laughter, statements of reassurance, and compliments directed toward the client and pet. In contrast, during problem appointments, 90% of the data gathering and client education focused on biomedical topics. Coders rated veterinarians as hurried during 30 of the 150 (20%) problem appointments; they rated clients as anxious during 39 (26%) problem appointments and as emotionally distressed during 21 (14%). CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that veterinarian-client-patient communication differed between wellness and problem appointments. Owing to the emphasis on biomedical content during problem appointments, veterinarians may neglect lifestyle and social concerns that could impact patient management and outcomes, such as client satisfaction and adherence to veterinarian recommendations.
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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 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".