Prevalence and types of pet-inclusive shelter services for unhoused survivors of intimate partner violence
Bibliographic record
Abstract
In 2018, the Agricultural Improvement Act section 12502 Protecting Animals with Shelter (PAWS) became law in the United States with a goal of reducing barriers to shelter for unhoused survivors of intimate partner violence (IPV) with pets. The purpose of this study was to describe the nature and extent of pet inclusive IPV shelter programming prior to the widespread implementation of PAWS to document a baseline from which to assess program growth in the post PAWS era. Using a qualitative description research design, respondents from 702 organizations completed either semi-structured interviews by telephone (n = 571) or written interviews by email (n = 131). Data was collected between June and October 2019 (approximately 6 months after the passage of PAWS) and were analyzed using directed content analysis methods. Although only 3% of shelters reported never receiving pet-related service requests, only 18.4% of shelters employed on-site co-housing programs for survivors with their pets. Details regarding aspects of on-site programs and other commonly employed modalities are described. Recommendations to enhance access to services for survivors seeking shelter with pets are provided.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".