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Psychiatr Serv ; 67(10): 1098-1102, 2016 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-27133724

RESUMO

OBJECTIVE: Accessing Supplemental Security Income (SSI) and Social Security Disability Insurance (SSDI) is challenging for people experiencing or at risk of homelessness. The federally funded SSI/SSDI Outreach, Access and Recovery (SOAR) project aims to improve access to SSI and SSDI through technical assistance (TA) to communities for promoting collaboration among agencies that serve this population and training professionals to assist people with applications. This study examined how SOAR TA has influenced community implementation efforts and the outcomes of these efforts. METHODS: Data were collected through site visits, a social network survey, and a management information system (MIS) in 13 states that began receiving TA in 2010 or 2011 and from Social Security Administration (SSA) administrative files on all applications (N=2,438,944) nationwide that were filed initially in fiscal year 2010. RESULTS: About 13% of trainees completed an application with the SOAR process. These trainees achieved a 73% approval rate for initial applications (including those approved after reconsideration), according to the MIS. Initial applications were approved almost twice as often among SOAR participants than among all homeless applicants (50% versus 28%), according to SSA. The odds of approval of the initial application were significantly higher (odds ratio=2.30) for SOAR participants compared with other homeless applicants, when other variables were held constant. CONCLUSIONS: Clients achieved better application outcomes when practitioners provided application assistance by using concepts from SOAR. However, training alone was not sufficient for successful SOAR implementation. Careful systems planning and dedicated staff resources are critical for improved outcomes.


Assuntos
Pessoas Mal Alojadas/estatística & dados numéricos , Seguro por Deficiência/estatística & dados numéricos , Assistência Pública/estatística & dados numéricos , Humanos , Previdência Social/estatística & dados numéricos , Estados Unidos
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