Veterinarian–Client Communication Skills: Current State, Relevance, and Opportunities for Improvement
Bibliographic record
Abstract
Communication is increasingly recognized as a core skill for veterinary practitioners, and in recent years, attention to communication competency and skills training has increased. To gain an up-to-date assessment of the current state of veterinary communication skills and training, we conducted a survey among veterinary practitioners in the United Kingdom and United States in 2012/2013. The questionnaire was used to assess the current state, relevance, and adequacy of veterinary communication skills among veterinary practitioners, to assess interest in further training, and to understand perceived challenges in communicating with clients. There was an overall response rate of 29.6% (1,774 of 6,000 recipients), with a higher response rate for UK-based practitioners (39.7%) than practitioners in the US (19.5%). Ninety-eight percent of respondents agreed that communication skills were as important as or more important than clinical knowledge. Forty-one percent of respondents had received formal veterinary communication skills training during veterinary school, and 47% had received training post-graduation. Thirty-five percent said their veterinary communication skills training during veterinary school prepared them well or very well for communicating with clients about the health of their pets, compared to 61% of those receiving post-graduate training. Forty percent said they would be interested in further veterinary communication skills training, with the preferred methods being simulated consultations and online training. While there has been increased emphasis on communication skills training during and after veterinary school, there is a need for more relevant and accessible training.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".