Farmer-veterinarian communication during herd health visits on dairy farms in Flanders, Belgium
Bibliographic record
Abstract
Well-developed clinical communication is crucial for dairy practitioners in providing effective herd health and production management (HHPM) advisory services, as they have potential to enhance farmer satisfaction and adherence to veterinary advice. However, there is limited knowledge regarding specific communication skills veterinarians use during HHPM visits. Understanding veterinarians' communication is essential for developing targeted educational interventions to enhance veterinarian-farmer interactions during HHPM visits. The objective of the study was, therefore, to investigate veterinarians' communication during HHPM visits on Flemish dairy farms. Dairy veterinarians audio-recorded HHPM visits on dairy farms in Flanders, Belgium. Composite communication processes were assessed using the Calgary-Cambridge Guide (CCG), and global scores and specific behavior counts were assessed with a modified Motivational Interviewing Treatment Integrity code (MITI). Twenty-seven participating veterinarians recorded 127 visits with 120 unique dairy farmers. The CCG communication processes "History taking," "Presenting information," and "Safety net and follow-up" were most prevalent. Veterinarians with ≤10 years practice experience provided the farmer with a "Safety net and a follow-up" more often than veterinarians with >10 years of experience. Other CCG processes such as "Agenda setting" and "Needs determination" were often lacking. However, veterinarians who had previously participated in communication skills training determined the needs of the farmer more often than participants who had not. Veterinarians who conducted ≥15 HHPM visits per month more fully performed "History taking" and "Creating a plan" than veterinarians with <15 HHPM visits per month. Participants displayed inconsistent efforts to incorporate "Partnership" and "Empathy." In modified MITI coded audio segments of 20 min, on average, veterinarians gave information 12 times, made a persuasive statement 3 times, asked 3 open questions, and 6 closed questions. This study indicated areas for improvement in dairy veterinarians' communication and highlighted the need for ongoing education and research in this area to enhance veterinary practice and animal health.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".