Learning-To-Communicate and Communicating-To-Learn in Veterinary Medicine: A Survey of Writing, Speaking, and Reading in Veterinary Medical Curricula
Bibliographic record
Abstract
This article reports the results of a survey of thirty-one colleges of veterinary medicine in the United States and Canada to identify common writing, speaking, and reading tasks performed by veterinary medical students and practicing veterinarians. From the twenty-seven colleges that responded (87% response rate), we learned that writing, speaking, and reading tasks are assigned in almost every veterinary medical course and that the communication tasks assigned in veterinary medical courses accord well with the communication tasks expected to be performed by practicing veterinarians. Along with these learning-to-communicate tasks, veterinary medical students are also assigned communicating-to-learn tasks. Unlike many of the writing-to-learn tasks associated with writing-across-the-curriculum programs, communicating-to-learn tasks in veterinary medical courses seem concerned with teaching students to think like veterinary medical practitioners. The emphasis on communication in veterinary medical curricula is probably due to some extent to the emphasis on problem-based learning, a curricular innovation popular in veterinary medical education. Problem-based learning requires that instruction be designed around cases or problems to be solved rather than topics or information to be covered. This merging of research and practice in the education of veterinary medical students may offer lessons for the education of professional practitioners in technical communication.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".