Supporting Positive Learning Experiences for Veterinary Students on Rotations in Remote Indigenous Communities in Canada
Bibliographic record
Abstract
Many health care programs in medicine, nursing, social work, and physiotherapy include practicum rotations near the end of students' studies. Increasingly, veterinary education programs also offer community-based rotations in underserved or remote communities. While these opportunities in veterinary medicine provide many learning benefits, they can also be stressful if the students do not feel adequately supported. The purpose of this study was to explore how veterinary students are and can be supported during rotations in remote Indigenous communities in Canada. Annually, four veterinary students from the University of Calgary Faculty of Veterinary Medicine travel with a small veterinary team to five communities in the Northwest Territories, Canada. During the 4-week rotation, students spend 2.5 weeks providing veterinary services to companion animals in these communities. In this study, 11/20 veterinary students who participated in this rotation between 2015 and 2020 completed online surveys. Results from this study suggest that participants of the rotation often felt welcomed and supported by the communities they served and were well supported by and connected to the members of the veterinary team. Findings are applicable across community-based veterinary student learning experiences and highlight the importance of building relationships with the communities being served, picking the right team, and implementing debriefing and decompressing activities during downtime.
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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 | 0.003 | 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 | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".