Bibliographic record
Abstract
Drawing on extensive evidence and experience in human medicine, this article offers a practical conceptual framework for thinking more precisely about how to teach and learn communication systematically and intentionally in veterinary medicine. The overarching goal is to promote the development of communication programs so as to improve communication in veterinary practice to a professional level of competence. A three-part conceptual framework is presented that first explores the rationale behind teaching and learning communication, including the evidence base regarding the impact of communication on clinician-client interactions and outcomes of care and the research on teaching and learning communication skills in medicine. The second part considers four ways to conceptualize what to teach and learn, as explicated by (a) the domains of communication in veterinary medicine; (b) ''first principles'' of effective communication; (c) evidence-based goals or outcomes for communication programs; and (d) delineation and definition of the specific individual skills that research evidence supports, as presented in the Calgary-Cambridge Guides. The last part of the conceptual framework examines how to teach communication, including the use of models, a primary focus on skill development as the backbone of communication programs, and the value of other methods supported by the evidence, such as simulated patients, videotape, small groups, and feedback and facilitation skills. Communication impacts the clinician- client interaction and outcomes of care in very significant ways. Communication can and should be taught and learned with as much rigor as other aspects of clinical competence. Veterinary programs at all levels should include the teaching of communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| 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".