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Abstract ObjectiveThe study tests a community- and data-driven approach to homelessness prevention. Federal policies call for efficient and equitable local responses to homelessness. However, the overwhelming demand for limited homeless assistance is challenging without empirically supported decision-making tools and raises questions of whom to serve with scarce resources. Materials and MethodsSystem-wide administrative records capture the delivery of an array of homeless services (prevention, shelter, short-term housing, supportive housing) and whether households reenter the system within 2 years. Counterfactual machine learning identifies which service most likely prevents reentry for each household. Based on community input, predictions are aggregated for subpopulations of interest (race/ethnicity, gender, families, youth, and health conditions) to generate transparent prioritization rules for whom to serve first. Simulations of households entering the system during the study period evaluate whether reallocating services based on prioritization rules compared with services-as-usual. ResultsHomelessness prevention benefited households who could access it, while differential effects exist for homeless households that partially align with community interests. Households with comorbid health conditions avoid homelessness most when provided longer-term supportive housing, and families with children fare best in short-term rentals. No additional differential effects existed for intersectional subgroups. Prioritization rules reduce community-wide homelessness in simulations. Moreover, prioritization mitigated observed reentry disparities for female and unaccompanied youth without excluding Black and families with children. DiscussionLeveraging administrative records with machine learning supplements local decision-making and enables ongoing evaluation of data- and equity-driven homeless services. ConclusionsCommunity- and data-driven prioritization rules more equitably target scarce homeless resources.
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Understanding service navigation pathways and service experiences among homeless populations
Previous homelessness research examined common pathways into homelessness, yet not much is known about how people navigate through services while experiencing homelessness. This study explored the service pathways of homeless individuals in the U.S. context, which show their connection with multiple organizations and their lived experiences of using services over time. We conducted 12 semi-structured in-depth interviews to grasp the history of service pathways, including the number of organizations, time gaps between services, and referral patterns. We also conducted participant observation shadowing with a subset of the study participants to understand how they interact with caseworkers. The length of service pathways varied, from less than five years to more than two decades. On average, participants went through at least three and up to eight organizations. Regarding service experiences, systemic- and individual-level themes were drawn for negative or positive experiences, such as strict organizational policies and the caseworker’s demeaning attitudes (negative), or supportive organizational culture and strong employee competencies (positive). The findings of this study provide deeper insights into homeless populations’ service trajectories and their experiences throughout the service-navigating process.
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- Award ID(s):
- 1737443
- PAR ID:
- 10368938
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Qualitative Social Work
- ISSN:
- 1473-3250
- Page Range / eLocation ID:
- Article No. 147332502211144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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