What Do Veterinary Students Value about Service Learning? Insights from Subsidized Clinics in an Urban Environment
Bibliographic record
Abstract
To promote accessible veterinary care in the community and to help students refine their communication skills, the University of Calgary, Faculty of Veterinary Medicine (UCVM) partnered with the Calgary Urban Project Society (CUPS), a human services organization, to develop the UCVM-CUPS Pet Health Clinics. These clinics are a service-learning experience where third-year students provide services to those facing barriers to veterinary care in Calgary, Alberta. The clinics are offered at CUPS for 6 weeks at 1 day per week. Each student participates in one 4-hour lab; running two 90-minute appointments. In this mixed-methods article, the question was asked: How does a communication-based veterinary service learning program impact students' perception and knowledge of their skills and their perceived role in community outreach to underserved populations and their animals? All third-year veterinary students (N = 30) participated in the 2018 UCVM-CUPS Pet Health Clinics. Students completed a demographics survey, and a pre- and post-clinic questionnaire. Statistical analysis was used to compare pre- and post-clinic responses, and to determine relationships between questionnaire responses and the demographics survey data. Students were significantly more in agreement with statements that demonstrated confidence in their medical knowledge, technical abilities, and communication skills after participating in the program. There was significantly more disagreement to continue volunteering after graduation, but many planned on providing community outreach. Overall, students felt a strong sense of social responsibility and were motivated to help underserved populations. The findings suggest that communication-based, service-learning experiences are related to greater social awareness and enhance students' clinical skills including communication.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".