A Multilevel Intervention Framework for Supporting People Experiencing Homelessness with Pets
Bibliographic record
Abstract
Approximately one in 10 people experiencing homelessness have pets. Despite the psychosocial benefits derived from pet ownership, systemic and structural barriers can prevent this group from meeting their basic needs and exiting homelessness. A multilevel framework is proposed for improving the health and well-being of pet owners experiencing homelessness. Informed by a One Health approach, the framework identifies interventions at the policy, public, and direct service delivery levels. Policy interventions are proposed to increase the supply of pet-friendly emergency shelters, access to market rental housing and veterinary medicine, and the use of a Housing First approach. At the public level, educational interventions are needed to improve knowledge and reduce stigma about the relationship between homelessness and pet ownership. Direct service providers can support pet owners experiencing homelessness by recognizing their strengths, connecting them to community services, being aware of the risks associated with pet loss, providing harm reduction strategies, documenting animals as emotional support animals, and engaging in advocacy. By targeting policies and service approaches that exacerbate the hardships faced by pet owners experiencing homelessness, the framework is a set of deliberate actions to better support a group that is often overlooked or unaccommodated in efforts to end homelessness.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".