Domestic Violence Shelter Partnerships and Veterinary Student Attitudes at North American Veterinary Schools and Colleges
Bibliographic record
Abstract
Animal abuse and domestic violence are linked issues, and pet ownership is reported to play a crucial role in the choice to leave an abusive situation. Although veterinarians witness the effects of abuse and violence over the course of their careers, they have limited training regarding these issues. One mechanism for educating veterinary students while providing a service for victims of domestic violence is the creation of partnerships between domestic violence shelters and veterinary schools. These extracurricular programs can provide both care for pets belonging to victims of domestic violence and an educational platform for student participants. The goals of this study were to determine the prevalence and characteristics of domestic violence shelter partnerships (DVSPs) at North American veterinary teaching hospitals and to determine whether the presence of a DVSP was associated with increased awareness among veterinary students regarding animal abuse and domestic violence. Nine of 33 veterinary schools surveyed described a DVSP program. Students at schools with DVSPs associated with their veterinary teaching hospitals were significantly more likely to indicate that their awareness of the link between animal abuse and domestic violence had increased during veterinary school. Most veterinary students reported that they felt poorly prepared to handle domestic violence and animal abuse issues in the workplace. This study indicates that extracurricular DVSPs are a viable means of educating veterinary students regarding domestic violence and animal abuse. A need for improved education on these topics in veterinary schools across North America is identified.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".