Applicability of the Calgary–Cambridge Guide to Dog and Cat Owners for Teaching Veterinary Clinical Communications
Bibliographic record
Abstract
Effective communication in health care benefits patients. Medical and veterinary schools not only have a responsibility to teach communication skills, the American Veterinary Medical Association (AVMA) Council on Education (COE) requires that communication be taught in all accredited colleges of veterinary medicine. However, the best strategy for designing a communications curriculum is unclear. The Calgary-Cambridge Guide (CCG) is one of many models developed in human medicine as an evidence-based approach to structuring the clinical consultation through 71 communication skills. The model has been revised by Radford et al. (2006) for use in veterinary curricula; however, the best approach for veterinary educators to teach communication remains to be determined. This qualitative study investigated if one adaptation of the CCG currently taught at Midwestern University College of Veterinary Medicine (MWU CVM) fulfills client expectations of what constitutes clinically effective communication. Two focus groups (cat owners and dog owners) were conducted with a total of 13 participants to identify common themes in veterinary communication. Participants compared communication skills they valued to those taught by MWU CVM. The results indicated that while the CCG skills that MWU CVM adopted are applicable to cat and dog owners, they are not comprehensive. Participants expressed the need to expand the skillset to include compassionate transparency and unconditional positive regard. Participants also expressed different communication needs that were attributed to the species of companion animal owned.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".