Spanish for Veterinarians Part 2: A Survey Gauging Student Perspectives on a Profession-Specific Language Learning Experience
Bibliographic record
Abstract
This article describes the distribution and results of a survey that was disseminated among students enrolled in Doctor of Veterinary Medicine (DVM) programs in the United States. It is a critical component to a substantial effort being undertaken at Colorado State University (CSU) to overhaul its current Spanish for Veterinarians offerings (outlined and discussed in "Spanish for Veterinarians Part 1: An Approach to Weaving Spanish Language Education into DVM Curricula") into a cohesive Spanish language program that offers consistent synchronous exposure to the language and guided practice over several semesters of instruction. The information obtained in this survey informs on veterinary student interest in and availability to engage in Spanish coursework created specifically for the veterinary field, as well as students' previous Spanish language learning experience. Additionally, it investigates the reasons motivating students' desire to participate in a Spanish for Veterinarians program, and their expectations and perspectives about receiving credit and paying for enrollment. It also includes students' online learning preferences and overall suggestions for optimal engagement in a Spanish language learning experience offered during DVM school. The anonymous results indicated that most respondents had taken Spanish only in high school, followed by those with one or two college-level courses. Interest in learning Spanish for the veterinary field is high and most students are willing to dedicate 2 to 4 hours weekly to language learning. This information guides curricular design decisions for a new Spanish for Veterinarians program that is currently being developed at CSU.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".