Barriers to Implementing On-Site Companion Animal Programs in U.S. Domestic Violence Shelters: Does Shelter Location Matter?
Bibliographic record
Abstract
Women in abusive relationships who are not able to take their companion animals (CAs) with them to domestic violence shelters report staying with their abusive partners longer. Many domestic violence shelters are therefore considering establishing CA programs to address this concern. However, little research has examined existing on-site CA programs, or the barriers shelters face in establishing them. The purpose of the current study was to investigate the barriers domestic violence shelter staff face in developing and implementing on-site CA programs. Contact was attempted with 1,740 domestic violence shelters across the United States, 702 shelters (40.3%) completed the survey through telephone interviews and online surveys, and 405 indicated that they did not have an existing on-site CA program in place. Results showed that health and safety (43.6%), space (40.6%), and resources (13.1%) were the most frequently reported barriers, that most shelters identified only one or two barriers, and that the nature of the primary barriers as well as the number of barriers endorsed did not significantly differ across rural versus urban locations or geographical regions of the United States ( p s > .05). Findings suggest there is a need for developing strategies for implementing on-site CA programs, and that these strategies can be used across the United States to help intimate partner violence survivors and their CAs seek safety.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".