Integrating Communication Skills, Awareness of Self and Others, and Reflective Feedback into One Inclusive Anatomical Representation of Relationship-Centered Health Care
Bibliographic record
Abstract
The American Veterinary Medical Association Council on Education mandates in standard 11 that all accredited colleges of veterinary medicine must develop and deliver formal processes by which students are observed and assessed in nine competencies. The eighth competency combines clinical communication and sensitivity toward soliciting and understanding individual narratives across a diverse clientele to facilitate health care delivery. Several frameworks have been designed to structure health care consultations for teaching and coaching purposes. The Calgary–Cambridge guide (CCG) provides an evidence-based approach to outlining the flow of consultations, incorporating foundational communication skills and elements of relationship-centered care into a series of sequential tasks. Although the CCG was intended for use as a flexible tool kit, it lacks visible connections between concrete experiences (e.g., the consultation) and reflective observation (e.g., the feedback). This teaching tip describes the development of a novel anatomical representation of the consultation that integrates process elements of the CCG with other core curricular concepts. By combining knowledge, technical skills, critical thinking, reflection, cultural humility, and self-awareness into a skeletal consultation model, linkages are established between communication and intergroup dialogue skills and diversity and inclusion (D&I). This model has been further adapted as feline, caprine, porcine, equine, avian, and reptilian versions for in-class use as strategic visual aids that highlight key areas of focus for Professional Skills class sessions. Future developments by the authors will explore how to link species-specific consultation models to assessment rubrics to reinforce the connection between content ( what) and process ( how).
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".