Developing Cultural Competence through the Introduction of Medical Spanish into the Veterinary Curriculum
Bibliographic record
Abstract
The AAVMC has prioritized diversity as one of its core values. Its DiVersity Matters initiative is helping veterinary medicine prepare for the changing demographics of the United States. One example of the changing demographics is the growing Hispanic population. In 2013, the Texas A&M University College of Veterinary Medicine & Biomedical Sciences responded to the needs of this growing sector by introducing medical Spanish into the core curriculum for Doctor of Veterinary Medicine (DVM) students. The medical Spanish course takes place over 5 weeks during the second year of the curriculum, and is composed of lectures and group learning. While this may seem like a very compressed time frame for language learning, our goal is to provide students with basic medical vocabulary and a limited number of useful phrases. In this paper, we outline the implementation of a medical Spanish course in our curriculum, including our pedagogical approaches to the curricular design of the course, and an explanation of how we executed these approaches. We also discuss the successes and challenges that we have encountered, as well as our future plans for the course. We hope that the successes and challenges that we have encountered can serve as a model for others who plan to introduce a foreign language into their curriculum as a component of cultural competency.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".