Coaching and Feedback: Enhancing Communication Teaching and Learning in Veterinary Practice Settings
Bibliographic record
Abstract
Communication is a critical clinical skill closely linked to clinical reasoning, medical problem solving, and significant outcomes of care such as accuracy, efficiency, supportiveness, adherence to treatment plans, and client and veterinarian satisfaction. More than 40 years of research on communication and communication education in human medicine and, more recently, in veterinary medicine provide a substantive rationale for formal communication teaching in veterinary education. As a result, veterinary schools are beginning to invest in communication training. However, if communication training is to result in development of veterinary communication skills to a professional level of competence, there must be follow-through with effective communication modeling and coaching in practice settings. The purpose of this article is to move the communication modeling and coaching done in the "real world" of clinical practice to the next level. The development of skills for communication coaching and feedback is demanding. We begin by comparing communication coaching with what is required for teaching other clinical skills in practice settings. Examining both, what it takes to teach others (whether DVM students or veterinarians in practice for several years) and what it takes to enhance one's own communication skills and capacities, we consider the why, what, and how of communication coaching. We describe the use of teaching instruments to structure this work and give particular attention to how to engage in feedback sessions, since these elements are so critical in communication teaching and learning. We consider the preconditions necessary to initiate and sustain communication skills training in practice, including the need for a safe and supportive environment within which to implement communication coaching and feedback. Finally we discuss the challenges and opportunities unique to coaching and to building and delivering communication skills training in practice settings.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".