Survey of US Veterinary Students on Communicating with Limited English Proficient Spanish-Speaking Pet Owners
Bibliographic record
Abstract
Veterinary schools and colleges generally include communication skills training in their professional curriculum, but few programs address challenges resulting from language gaps between pet owners and practitioners. Due to shifting US demographics, small animal veterinary practices must accommodate an increasing number of limited English proficient (LEP) Spanish-speaking pet owners (SSPOs). A national survey was conducted to assess the interest and preparedness of US veterinary students to communicate with LEP SSPOs when they graduate. This online survey, with more than 2,000 first-, second-, and third-year US veterinary students, revealed that over 50% of students had worked at a practice or shelter that had LEP Spanish-speaking clients. Yet fewer than 20% of these students described themselves as prepared to give medical information to an LEP SSPO. Over three-fourths of respondents agreed that communication with LEP SSPOs was important for veterinarians in general, and two-thirds agreed that communication with LEP SSPOs was important for themselves personally. Ninety percent of students who described themselves as conversant in Spanish agreed that they would be able to communicate socially with SSPOs, while only 55% said they would be able to communicate medically with such clients. Overall, two-thirds of students expressed interest in taking Spanish for Veterinary Professionals elective course while in school, with the strongest interest expressed by those with advanced proficiency in spoken Spanish. Bridging language gaps has the potential to improve communication with LEP SSPOs in the veterinary clinical setting and to improve patient care, client satisfaction, and the economic health of the veterinary profession.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".