Communication skills and the veterinarian-client-patient relationship
Bibliographic record
Abstract
This thesis is an investigation of veterinarian-client-patient communication among companion animal veterinarians in Southern Ontario, using a cross-sectional study design. A random sample of 50 veterinarians was recruited and all clients that presented to the practice during the researcher's visit were invited to participate. A total of 300 appointments were videotaped, six appointments per veterinarian, including three wellness and three problem appointments. The Roter Interaction Analysis System (RIAS), a widely used quantitative coding system, was applied to veterinarian-client-patient communication. Veterinarians completed surveys to provide demographic characteristics and measures of self-esteem (Rosenberg Self-Esteem Scale), empathy (Interpersonal Reactivity Index) and visit satisfaction (Physician Satisfaction Scale). An exit interview was conducted to assess the impact of videotaping on veterinarian performance. In addition, surveys were administered to clients to obtain demographic characteristics and client satisfaction. Veterinarians utilized primarily closed-ended questions to gather data from clients and only a small amount of conversation was devoted to building rapport and establishing a partnership with the client. Two communication patterns were identified and pattern use was dependent on the type of appointment. Veterinarians primarily used the biolifestyle/social pattern (69%) in wellness appointments and the biomedical pattern (85%) in problem appointments. Communication dynamics differed in wellness and problem appointments. Wellness appointments were relationship-centered with a broad range of topics of discussion (lifestyle activities, social interactions, and anticipatory guidance topics). The emphasis was on rapport building and social conversation and reflected high levels of interaction with the pet in a relaxed atmosphere. Problem appointments were characterized as narrowly focused on biomedical information exchange in a tense emotional atmosphere. The majority of study veterinarians (74%) were highly satisfied with their appointments and veterinarians reported increased satisfaction with wellness appointments compared to problem appointments. Potential determinants of visit satisfaction differed for wellness and problem appointments. The findings of this study suggest that some veterinarians may not utilize all of the tools they need for effective communication. Based on evidence in human medicine, the clinical implications of these results is a lost potential to enhance client satisfaction and adherence, improve pet health, increase veterinarian satisfaction and reduce malpractice complaints.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".