Evaluation of a Student-Led Community-Based Veterinary Clinic for Disabled Low-Income Clients: A Case Study of the University of Florida PAWS Program
Bibliographic record
Abstract
People with disabilities experiencing low socioeconomic position are priority populations when considering access to veterinary care. In this population, intersectional inequities lead to adverse health outcomes for both those individuals and the companion animals they care for. Community-based veterinary clinics provide an opportunity to target these inequities from a culturally sensitive lens, intending to improve human and animal outcomes. We conducted a process evaluation of a student-led community-based clinic for this population to better understand client satisfaction, assess learning outcomes among veterinary students, and improve program delivery and services. During academic year 2020-2021, the monthly clinics had 162 appointments in total with a median 15 Doctor of Veterinary Medicine (DVM) candidates volunteering at each clinic. Clients and volunteers responded to survey questionnaires designed to elicit information about their experiences with the clinic, including open-ended questions for further elucidation of measurable indicators of client-, patient-, and student-level impact. Clients attributed enrollment in the clinic with improved quality-of-life and reduction of financial burden; the program saved clients approximately $2,050 per pet during the evaluation year. Furthermore, the clinic widely facilitated completion of the college's core Primary Care and Dentistry learning outcomes. Beyond curriculum-standard learning objectives, students also reported positive attitude changes and increased readiness to provide care to people with disabilities and people experiencing low socioeconomic position. The results of this evaluation have significant implications for both veterinary and public health pedagogy. Especially, they highlight the significance of community health practice in veterinary trainee education.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".