Addressing Five Explanations for the Lack of Racial and Ethnic Diversity in Veterinary Medicine in the United States: Results from a Survey of DVM Students and Early Career Veterinarians
Bibliographic record
Abstract
According to the United States Bureau of Labor Statistics, veterinary medicine is one of the least racially and ethnically diverse professions in the United States. Drawing from past research in veterinary health, as well as science, technology, engineering, and math (STEM) fields more broadly, we designed and implemented an online survey to address five nonmutually exclusive explanations for the lack of racial/ethnic diversity in veterinary medicine. On the basis of the responses of 2,083 participants to the survey, we found consistent, statistically significant differences in the experiences and perceptions of well-represented compared to under-represented DVM students and recent graduates. These differences correspond to aspects of each of the five potential explanations for the lack of diversity in veterinary medicine examined in this study, highlighting the complex nature of this issue. Most notably, our results suggest precollege exposure to advanced STEM courses, increased accessibility to paid experiential positions, pre-professional mentorship and fostering a sense of professional identity are particularly important areas of focus for organizations and institutions interested in targeting barriers to diversity in veterinary medicine.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".