Student Veterinarian Perceptions of Community-Based Primary Care Veterinary Clinics in Indigenous Communities in Southern Ontario, Canada
Bibliographic record
Abstract
Community-based primary care veterinary clinics represent an opportunity to benefit multiple populations. Student veterinarians are afforded the opportunity to build technical and non-technical professional skills, while underserved communities are provided with access to companion animal care. The Ontario Veterinary College (OVC), as with many other veterinary colleges across Canada and the United States, has hosted community-based primary care veterinary clinics, including in local Indigenous communities. As these clinics continue and grow, it is critical to evaluate their operation to ensure that they align with community goals and values, adequately support student learning, and do not perpetuate racism and implicit bias. The objective of this study was to explore the perceptions of student veterinarians who had volunteered at community-based primary care veterinary clinics in First Nations communities in southern Ontario, Canada. We used an online survey that consisted of multiple choice and short answer questions focused on motivating factors for involvement, supports available before and during the clinics, exposure to Indigenous cultures, and the challenges and rewards associated with volunteering. Forty-one student veterinarians from OVC completed the survey in January 2020. Most students were motivated to volunteer to make a positive difference in the lives of people and animals and improve their clinical skills. In general, respondents felt adequately prepared for and supported during their experiences but did recommend additional pre-departure instructions on roles and responsibilities. Participants were ambivalent as to whether they had learned about Indigenous culture, with several strongly recommending more cultural sensitivity training. Most students found their experiences rewarding because of the gratitude expressed by clients and the feeling that they had made a difference. We reflect on the potential benefits and challenges of community-based primary care veterinary clinics in light of student responses.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".