Clinical communication patterns of veterinary practitioners during dairy herd health and production management farm visits
Bibliographic record
Abstract
Effective communication with clients is an important skill for veterinary practitioners and is linked to clients' satisfaction with the consultancy and adherence to medical advice. Detailed description of veterinary communication styles in companion animal contexts has added to the communication knowledge base and informed veterinary curricula and postgraduate education programs. The objective here was to describe veterinary practitioners' communication patterns during dairy herd health and production management farm visits. Fourteen veterinarians in Alberta and Ontario, Canada, were provided with action cameras (Hero3 black edition; GoPro Inc., San Mateo, CA) and recorded 3 to 7 farm visits each. The resulting 70 audio-video recordings were analyzed using the Roter Interaction Analysis System. Additionally, demographic variables were obtained from study participants to investigate associations of these variables with communication patterns. Intraclass correlation coefficients were calculated for communication units, and multilevel negative binomial regression was used to compare communication patterns between demographic groups. Additionally, the relationship-centeredness of interactions was evaluated and compared between groups using linear regression models. Communication patterns varied considerably among recordings of the same veterinarian interacting with different clients. However, most veterinary talk focused on farmer education, counseling, and building a relationship, whereas especially open-ended questions were rare. When discussion revolved around health issues of an individual animal, veterinarians used less social talk but focused more on biomedical information gathering. Veterinarians' age and sex and the length of the veterinarian-farmer relationship had limited association with communications; however, if the farmer and veterinarian were both male, the conversation was more relationship centered. Communication of veterinarians with previous communication training was very similar to that of those without training. Detailed description of veterinary practitioners' communication patterns on dairy farms will contribute to establishing the importance of communication as a clinical skill and is critical to identifying unique aspects of veterinary on-farm communication.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".