Students’ Reports of Challenges, Experiences, and Perceptions of Equity, Diversity, and Inclusion at Veterinary Colleges in Canada and the United States
Bibliographic record
Abstract
Equity, diversity, and inclusion (EDI) in veterinary medicine affects veterinarians, students, clients, and the regional availability of veterinary services. Veterinary students from 5 colleges in Canada and 5 colleges in the United States were surveyed about their challenges, experiences, and perceptions related to EDI, resulting in 456 responses (10.4%). A greater proportion of participants reported personal, financial, mental health, and physical health challenges during veterinary college compared with the proportion reporting these challenges before starting veterinary college. Statistically, participants who did not identify as white (odds ratio [OR]: 2.2, confidence interval [CI]: 1.1-4.3), who reported having a disability (OR: 5.0, CI: 2.1-12.1), and who identified as part of the LGBTQ2S+ community (OR: 8.5, CI: 3.8-19.2 ) were more likely to agree or strongly agree that discrimination occurs at veterinary colleges. Fewer participants reported experiencing discrimination in veterinary colleges (20.6%) compared with veterinary workplaces (36.8%). In the workplace, participants reported the expectation of facing bias more from clients than from managers or peers. The expectation of facing bias from clients was associated with the female gender (OR: 2.7, CI: 1.3-5.6), not identifying as white (OR: 7.4, CI: 2.5-21.3), and identifying as part of the LGBTQ2S+ community (OR: 2.7, CI: 1.3-5.4). More participants expected to practice in the same type of region as where they grew up rather than a different type of region. Thus, training future veterinarians from areas with region-based lack of access to veterinary services may be more effective than simply training more veterinarians. College EDI initiatives should include input from all participants and especially those who are most likely to experience discrimination, facilitating meaningful training and support.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.000 | 0.000 |
| 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".