Integrated, Scaffolded, and Mandatory Community and Shelter Medicine Curriculum: Best Practices for Transformational Learning on Access to Veterinary Care
Bibliographic record
Abstract
Within veterinary medical education, there is increasing focus on equity and cultural competency/humility, especially within service learning in community and shelter medicine. This article reviews the current literature and draws from the experience of the Ontario Veterinary College Community Healthcare Partnership Program's development of a community and shelter medicine curriculum. We propose that to graduate veterinarians with the knowledge and skills to address inequities in access to veterinary care, a best practice is to integrate mandatory in-class and experiential learning activities, scaffolded across the curriculum. This is a best practice as it creates the best chance for transformational learning for students and is part of our responsibility to the communities we partner with to move toward cultural safety. This Best Practices report addresses the following questions: (a) What foundation of knowledge in community and shelter medicine is needed? (Five curricular pillars: animal welfare, vulnerable animals, spectrum of care, well-being, and cultural humility); (b) How should programs be structured? (Mandatory, integrated, and scaffolded curriculum); and (c) What are the pedagogical goals? (Transformational learning). It is our hope that this synthesis is of value to other veterinary colleges seeking to develop programs and/or curricula in community and shelter medicine to address barriers to veterinary care access.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".