Evaluation of Veterinary Students’ Communication Skills with a Service Dog Handler in a Simulated Client Scenario
Bibliographic record
Abstract
Effective communication skills serve as a key component of excellent veterinary care and provide a foundation for building trusting relationships with clients. While many veterinary clients value their pets for companionship, the focus of other relationships may be based on a partnership between the human and animal, as is the case with the handlers of service dogs. As the use of service dogs in the US continues to grow, it is important that veterinary professionals are educated on how best to meet the unique needs of service dogs and their handlers. This article evaluates the interactions of veterinary students with a service dog handler in a simulated client scenario. Ten videotaped interactions were coded to assess third-year students’ communication skills (nonverbal communication, open-ended questions, reflective listening, and empathy), and their ability to discuss the diagnostic and therapeutic options for a dog with suspected intervertebral disk disease. Results showed that the majority of students demonstrated competence in the use of nonverbal communication skills and in discussing the biomedical aspects of the disease. Students require development in the use of open-ended questions, reflective listening statements, and expression of empathy, as well as building client rapport and discussing the psychosocial aspect of the disease on the client and patient. These findings suggest that veterinary students may benefit from targeted instruction on “best practices” in caring for service dogs and their handlers, including greater attention to the psychosocial aspects of a disease, and from additional communication practice using standardized clients with service dogs.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".