Exploring the Experiences of Visiting Veterinary Service Providers in Indigenous Communities in Canada: Proposing Strategies to Support Pre-Clinical Preparation
Bibliographic record
Abstract
Many Indigenous communities in Canada lack access to veterinary services due to geography, affordability, and acceptability. These barriers negatively affect the health of animals, communities, and human-animal relationships. Canadian veterinary colleges offer veterinary services to Indigenous communities through fourth-year veterinary student rotations. Ensuring that the students and other volunteer veterinary service providers (VSP) are adequately prepared to provide contextually and culturally appropriate care when working with Indigenous peoples has not been explicitly addressed in the literature. We explored the experiences of VSP delivering services in unfamiliar cultural and geographic settings and identified: what pre-clinic training was most helpful, common challenges experienced, and personal and professional impacts on participants. Fifty-two VSP (veterinarians, animal health technicians and veterinary students) who participated in clinical rotations offered by five Canadian veterinary colleges between 2014 and 2022 completed online surveys. Respondents shared their pre-clinic expectations, sense of preparedness to practice in a remote Indigenous community, their clinical and community experiences, and any personal and professional impacts from the experience. Data were analyzed using a directed content analysis approach. Respondents highlighted which pre-clinic training was most valuable and what they felt unprepared for. Community infrastructure and resources were concerns and many felt unprepared for the relational and communication barriers that arose. VSP were uncomfortable practicing along a spectrum of care with limited clinical resources. Many VSP identified positive personal and professional impacts. Our findings suggest that pre-clinic orientations focused on contextual care in limited resource settings could better prepare VSP to serve underserved Indigenous communities.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".