Training Veterinary Students in Shelter Medicine: A Service-Learning Community-Classroom Technique
Bibliographic record
Abstract
Shelter medicine is a rapidly developing field of great importance, and shelters themselves provide abundant training opportunities for veterinary medical students. Students trained in shelter medicine have opportunities to practice zoonotic and species-specific infectious disease control, behavioral evaluation and management, primary care, animal welfare, ethics, and public policy issues. A range of sheltering systems now exists, from brick-and-mortar facilities to networks of foster homes with no centralized facility. Exposure to a single shelter setting may not allow students to understand the full range of sheltering systems that exist; a community-classroom approach introduces students to a diverse array of sheltering systems while providing practical experience. This article presents the details and results of a series of 2-week elective clinical rotations with a focus on field and service learning in animal shelters. The overall aim was to provide opportunities that familiarized students with sheltering systems and delivered primary-care training. Other priorities included increasing awareness of public health concerns and equipping students to evaluate shelters on design, operating protocols, infectious disease control, animal enrichment, and community outreach. Students were required to participate in rounds and complete a project that addressed a need recognized by them during the rotation. This article includes costs associated with the rotation, a blueprint for how the rotation was carried out at our institution, and details of shelters visited and animals treated, including a breakdown of treatments provided. Also discussed are the student projects and student feedback on this valuable clinical experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".