Teaching Tip: Improving Students' Email Communication through an Integrated Writing Assignment in a Third-Year Toxicology Course
Bibliographic record
Abstract
Client communication is important for success in veterinary practice, with written communication being an important means for veterinarian-client information sharing. Effective communication is adapted to clients' needs and wants, and presents information in a clear, understandable manner while accounting for varying degrees of client health literacy. This teaching tip describes the use of a mock electronic mail assignment as one way to integrate writing into a required veterinary toxicology course. As part of this project, we provide baseline data relating to students' written communication that will guide further development of writing modules in other curricula. Two independent raters analyzed students' writing using a coding scheme designed to assess adherence to the guidelines for effective written health communication. Results showed that the majority of students performed satisfactorily or required some development with respect to recommended guidelines for effective written health communication to facilitate client understanding. These findings suggest that additional instruction and practice should emphasize the importance of incorporating examples, metaphors, analogies, and pictures to create texts that are comprehensible and memorable to clients. Recommendations are provided for effective integration of writing assignments into the veterinary medicine curriculum.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".