Raising campus awareness on issues of globalization in veterinary medical education
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Ross University School of Veterinary Medicine (RUSVM), due to its geographic location, provides an opportunity to raise awareness regarding issues of globalization in veterinary medical education, specifically in relation to diversity and acculturation. This manuscript discusses RUSVM's demographics and raises awareness concerning challenges North American students may experience when immersed in an environment where the racial mix of the university is predominantly White, vastly different than the community in which it resides.RUSVM students, faculty and support staff (n=1448) were invited to complete the American Veterinary Medical Association climate survey. Survey response rate was 36%. Students and faculty self-identified as White (80% and 76%, respectively), and support staff self-identified as African American or Black (71%). Non-US Faculty reported a legal residence of Europe 8%, Africa 2%, or the Caribbean (44%), and support staff of Saint Kitts and Nevis (68%). Non-US students, most often indicated Canadian residency. Qualitative analyses resulted into three themes addressing university climate (35%), culture privilege (42%), and professionalism (24%). Matriculation of North American students wishing to study abroad should include deliberate discussions with respect to diversity, cultural and social contexts supporting acculturation, and adaptation to a broader academic environment.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.010 |
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".