Preliminary assessment of a tool for measuring relationship-centred communication in veterinary consultations (adapted VR-COPE)
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Relationship-centred communication is considered a desirable goal in veterinary medicine, and a number of different tools have been developed to measure relationship-centred communication. This study was designed as an initial assessment of an adapted version of the Verona Patient-centred Communication Evaluation (VR-COPE) scale, originally developed for medical communication research, to evaluate its potential for measuring relationship-centredness in veterinary consultations. Fifty-five consultations in the United Kingdom and United States of America were videotaped and analysed. The median VR-COPE total score (out of a potential 100 points) was 76.00 for all consultations. The highest overall score was for "Structuring" (of the consultation), whereas the lowest scores were for "Client Worries," "Psychological Impact," and "Empathy." This initial assessment of the adapted VR-COPE suggests it may be helpful in measuring content, process, and structuring skills related to relationship-centredness in veterinary consultations. It may also help uncover aspects of relationship-centredness that are unique from those uncovered by other tools. Further research is needed to fully assess the role of VR-COPE in veterinary communication research and the contributions it can make to relationship-centredness in veterinary consultations.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".