Skip to main content
Full access
Articles
Published Online: 31 October 2014

Fidelity to the Housing First Model and Effectiveness of Permanent Supported Housing Programs in California

Abstract

Objectives:

Permanent supported housing programs are being implemented throughout the United States. This study examined the relationship between fidelity to the Housing First model and residential outcomes among clients of full service partnerships (FSPs) in California.

Methods:

This study had a mixed-methods design. Quantitative administrative and survey data were used to describe FSP practices and to examine the association between fidelity to Housing First and residential outcomes in the year before and after enrollment of 6,584 FSP clients in 86 programs. Focus groups at 20 FSPs provided qualitative data to enhance the understanding of these findings with actual accounts of housing-related experiences in high- and low-fidelity programs.

Results:

Prior to enrollment, the mean days of homelessness were greater at high- versus low-fidelity (101 versus 46 days) FSPs. After adjustment for individual characteristics, the analysis found that days spent homeless after enrollment declined by 87 at high-fidelity programs and by 34 at low-fidelity programs. After adjustment for days spent homeless before enrollment, days spent homeless after enrollment declined by 63 at high-fidelity programs and by 53 at low-fidelity programs. After enrollment, clients at high-fidelity programs spent more than 60 additional days in apartments than clients at low-facility programs. Differences were found between high- and low-fidelity FSPs in client choice in housing and how much clients’ goals were considered in housing placement.

Conclusions:

Programs with greater fidelity to the Housing First model enrolled clients with longer histories of homelessness and placed most of them in apartments.
There is increasing recognition of the importance of choice and self-determination in housing for persons with psychiatric disabilities who have experienced housing instability and homelessness. Studies of consumer preference have demonstrated that most individuals prefer to live in independent living arrangements, such as scatter-site apartments, and recent advocacy efforts also have focused on providing consumers with access to less restrictive and more integrated housing arrangements (1,2). More traditional supervised, congregate residences have been criticized for using housing as leverage for treatment, limiting tenancy rights, and sustaining the social segregation of persons with mental illness (3,4). In contrast, the Housing First model of permanent supported housing provides homeless individuals with immediate access to housing and access to both a treatment team and community supports that provide flexible, client-driven services (5). The Pathways model of Housing First emphasizes the provision of scatter-site apartments in areas of the community apart from where services are provided as a means of honoring consumer choice, facilitating broader community integration, and fostering an identity as a “tenant” rather than a “patient” (6).
Although all Housing First programs provide immediate access to supported housing and intensive services, substantial variation exists in their approaches to housing and treatment and in their levels of client choice and client involvement (7). Housing First programs have been implemented in areas as diverse as Oakland, California; Philadelphia; Phoenix, Arizona; and Brattleboro, Vermont. More recently, the Housing First model has spread rapidly across Canada and Europe, where it has been adapted to both local environments and specific populations (810). However, there is no clear understanding of the extent to which supported housing programs can improve residential outcomes by departing from the Housing First model.
A recent policy experiment in California involving the large-scale implementation of permanent supported housing programs provided an opportunity to examine the relationship between fidelity to the Housing First model and residential outcomes. On November 2, 2004, California voters approved the Mental Health Services Act (MHSA), which applied a tax of 1% on incomes over $1 million to fund public mental health services (11). The cornerstone of the MHSA is full service partnerships (FSPs): combined housing and team-based treatment programs that do “whatever it takes” to improve residential stability and mental health outcomes among persons with serious mental illness who are homeless or at risk of homelessness (12). Consistent with prior efforts to reform the delivery of mental health care in California, the MHSA emphasizes concepts of services integration, recovery orientation, and permanent housing, features that have significant overlap with the core elements of Housing First.
The MHSA’s emphasis on a vision of recovery-oriented care that does “whatever it takes,” the flexibility in funding, and the influence of stakeholders, combined with a lack of specificity and oversight regarding expected FSP practices, led to the implementation of a diverse set of FSPs (7,13). In this study, we examined the relationship between FSP practices that are consistent with fidelity to the Housing First model and the effectiveness of permanent supported housing programs.

Methods

This study had a mixed-methods design. Quantitative administrative and survey data were used to describe FSP practices and examine the relationship between fidelity to the Housing First model and changes in housing across a large number of FSP programs. Focus groups of clients provided qualitative data to enhance our understanding of these findings; the data included accounts by clients of housing-related experiences in a subset of high- and low-fidelity programs.

Implementation under the MHSA

The FSP programs in California provide individuals with serious mental illness who are homeless or at risk of homelessness with subsidized permanent housing and multidisciplinary team-based services with a focus on rehabilitation and recovery. FSP services typically follow either an intensive case management model or a modified assertive community treatment model (14). Clients are recruited through outreach and referrals involving psychiatric hospitals, emergency rooms, other mental health programs, county agencies, jails, shelters, rescue missions, and service providers working on the streets. Most FSPs deliver services to clients in real-world settings, such as their home or workplace and other places in the community chosen by the client or deemed of therapeutic value by staff. Crisis intervention services are available 24 hours a day, seven days a week.

Fidelity to the Housing First model

Because there was no existing conceptual framework to describe FSP practices, a framework was developed to compare FSP practices with the Housing First model, a benchmark program sharing similar goals, vision, and structure. Housing First programs provide immediate access to affordable, permanent, scatter-site housing with tenancy rights and access to team-based services designed in accordance with a recovery-oriented service philosophy, which draws heavily on the psychosocial rehabilitation model (15). Key elements of this approach are consumer choice, self-determination, and independence; the active use of harm reduction, motivational interviewing, assertive engagement, and person-centered planning by program staff; and the absence of coercive practices.
Fidelity was measured by using the Housing First Fidelity Survey (16). The survey measures fidelity to the Housing First model across two factors and five domains. One factor measures fidelity with respect to housing choice and structure, separation of housing and services, and service philosophy. A second factor measures fidelity with respect to service array and team structure. This article focuses on the first factor because its three associated domains are closely associated with the FSP programs’ approach to housing.

Study sample

The California Department of Mental Health Data Collection and Reporting (DCR) system was used to identify FSP clients and to summarize days spent in various residential settings from one year preenrollment to one year postenrollment. The sample included individuals with serious mental illness (defined as schizophrenia or schizoaffective disorder, bipolar disorder, or major depressive disorder) who were enrolled in participating FSPs between January 1, 2005, and June 30, 2009. Clients who were not enrolled for at least 180 days were excluded from the analyses. Information about the clients’ history of housing was identified from an FSP assessment form, and changes in residential status over time were derived from key-event tracking forms (12). Residential settings included homeless (includes living in one’s car), emergency shelter, and temporary housing (includes living with a friend but not paying rent); justice system; congregate or residential (group arrangements, most commonly group living homes and board and care residences); apartment or single-room occupancy (SRO) hotel; residences of parents, family, and others; and unknown. Thus these living arrangements represent a range of options, from homelessness to shared, highly supervised, and structured congregate settings and more independent settings, such as apartments and SRO hotels.

Quantitative analyses

Changes in days in residential settings in the overall sample and by level of program fidelity were examined by using paired t tests. Factor scores derived from the Housing First Fidelity Survey (16) of 93 FSP programs were used to rank the programs on fidelity to the Housing First model with respect to housing choice and structure, separation of housing and services, and service philosophy. On the basis of our knowledge of the programs and an examination of natural cut points, programs with factor scores in the top 20% were designated as having high fidelity to the Housing First model, and programs with factor scores in the bottom 20% were designated as having low fidelity; the remaining programs were designated as having mid-fidelity. If a client was enrolled in an FSP for fewer than 365 days in the postenrollment period, the number of days in each residential setting was first annualized by dividing by the days enrolled in the FSP and multiplying by 365.
Changes in days in residential settings by level of program fidelity were estimated by using a series of generalized estimating equations (GEEs) specified with a Gaussian distribution and identity link, an exchangeable correlation matrix to account for correlated errors within FSP programs, and an exposure offset to adjust for differences in enrollment in the postenrollment period (17). GEEs were estimated for each residential setting, where the dependent variable was the change in the number of days in the residential setting from the preenrollment to the postenrollment period and the main independent variables of interest were indicator variables for each level of fidelity (the intercept was suppressed). Two sets of GEEs were estimated. One set controlled for the following individual characteristics: age, gender, race-ethnicity, clinical diagnosis, diagnosis of a substance use disorder, and Medicaid coverage. A second set controlled for individual characteristics and days in the residential setting in the preenrollment period. All analyses were conducted in Stata, version 12 (18).

Qualitative analyses

Site visits were conducted at 20 programs that were purposefully sampled by using a maximum variation strategy (19) to provide geographic, political, and economic diversity as well as a wide range of scores on the Housing First Fidelity Survey (13,16). Sampling was a two-stage process. Participating counties were selected from southern, central, and northern California, including coastal and inland and urban and rural counties. Within counties, programs were selected to maximize the range of fidelity scores. Site visits involved observation, interviews with program staff, and focus groups with clients.
Summaries of audio recordings of client focus groups created during the site visits at the five highest and five lowest fidelity sites were reviewed for insights into clients’ experiences. The qualitative data were used to complement the quantitative data by providing a depth of understanding not available by using the quantitative data alone (20). A template or matrix approach (21,22) to text analysis was used to identify specific housing topics that were discussed by each focus group and to compare how the programs addressed topics discussed by both sets of focus groups.

Results

Client characteristics

A total of 86 FSPs with 7,186 clients provided DCR data for this study; 602 clients were excluded because they had been enrolled in the FSP for fewer than six months. Demographic and clinical characteristics of 6,584 FSP clients by level of program fidelity are shown in Table 1. The mean±SD age was 40±14; 2,988 (45%) were female; 2,537 (39%) were non-Latino white, 967 (15%) Latino, 901 (14%) African American, 241 (4%) Asian, and 1,938 (29%) other or unknown race-ethnicity; 3,973 (60%) had a diagnosis of schizophrenia, 1,406 (21%) bipolar disorder, and 1,205 (18%) major depressive disorder; 3,355 (51%) received a diagnosis of a substance use disorder; and 4,855 (73%) had Medicaid coverage prior to enrollment in the FSP. Compared with clients in lower fidelity programs, clients in high-fidelity programs were more likely to be age 60 or older, more likely to be non-Latino white, less likely to have schizophrenia, and more likely to have a substance use disorder.
Table 1 Characteristics of 6,584 clients in full service partnerships, by level of program fidelity to the Housing First model
 Low fidelity (N=1,245)Midfidelity (N=3,481)High fidelity (N=1,858) 
CharacteristicN%N%N%p
Age      <.001
 18–24249206001735819 
 25–59956772,480711,33472 
 60403401121568 
Gender      .596
 Female570461,5594585946 
 Male675541,9155599754 
Race-ethnicity      <.001
 Non-Latino white429341,3343877442 
 African American180144311229016 
 Latino241194471327915 
 Asian28210031136 
 Other or unknown367291,1693440222 
Clinical diagnosis      .046
 Schizophrenia766622,126611,08158 
 Bipolar disorder277227072042223 
 Major depression202166481935519 
 Substance use disorder      .002
  Yes624501,720491,01154 
  No621501,7615184746 
Insurance      .007
 Medicaid895722,626751,33472 
 Uninsured350288552552428 

FSP program characteristics

Table 2 shows FSP program characteristics by level of program fidelity. In general, high-fidelity programs were more likely than low-fidelity or midfidelity programs to meet fidelity thresholds for housing choice and structure and separation of housing and services. High-fidelity and midfidelity programs were more likely than low-fidelity programs to meet fidelity thresholds with respect to service philosophy.
Table 2 Characteristics of 86 full service partnerships, by level of program fidelity to the Housing First model
 Low fidelity (N=20)Midfidelity (N=50)High fidelity (N=16) 
CharacteristicN%N%N%p
Housing choice and structure       
 Fewer than 30% of participants live in emergency, short-term, transitional, or time-limited housing105035701381.116
 At least 85% of participants live in scatter-site permanent supported housing15510531.042
Separation of housing and services       
 Access to permanent housing requires only face-to-face visits with program staff and adhering to a standard lease15173416100<.001
 A majority of participants in permanent housing have a lease or occupancy agreement that specifies their rights and responsibilities of tenancy and that does not include provisions regarding adherence to medication, sobriety, or a treatment plan or adherence to program rules such as curfews or restrictions on overnight guests52513261275.001
Service philosophy       
 Participants have the right to choose, modify, or refuse services and supports at any time1518361488<.001
 Participants with serious mental illness are not required to take medication or participate in treatment15397816100<.001
 Participants with substance use disorders are not required to participate in substance use treatment630469216100<.001
 Program follows a harm reduction approach to substance use15479416100<.001

FSP client experiences

Qualitative data from the focus groups revealed differences between high- and low-fidelity FSPs in client choice in housing and the degree to which client-driven goals were considered in determining housing placement. Clients of high-fidelity FSPs described being given choices among apartments and locations: “[The FSP] gave me opportunities to look at different places, but I picked the one on [Name] Street because I feel that’s what I wanted.” In contrast, clients in low-fidelity programs reported that they were simply assigned to housing by the FSP: “There was really no decision. It’s what I was offered: ‘You can stay here or you can stay on the streets.’ . . . It was just offered to me, board and care. . . . I don't know if I had any other choices.”
Clients of high-fidelity programs reported that FSPs helped them to find housing that met their individual needs or helped them work toward their personal goals. Thus a client described the FSP’s helping her find housing that would support her goal of reuniting with her family: “I’ve been in [City] for over a year and a half [in a one-bedroom apartment]. . . . I specifically requested [City] because part of my recovery was to re-establish my relationship with my kids. And my kids are in [City], so that’s why I’m still in [City].” In another high-fidelity program, a client talked about how the FSP found him an apartment that was convenient to transportation and the downtown business district of the city. As he put it, “It's a small apartment, very small . . . it’s 300 square feet, but I've got a refrigerator; it's nice. . . . I’m looking for work and it’s central . . . that was one of the considerations so that I would be able to find work and get to it.” Participants in the focus groups at the low-fidelity programs did not tell any stories that reflected the role of self-determination and client-driven goals in housing placement.

FSP clients’ housing outcomes

Table 3 shows the average number of days that FSP clients spent in various living situations in the year before and after enrollment, both overall and by level of program fidelity. Overall, days spent homeless declined by 47, days spent in justice system settings declined by 10, and days spent with parents or family members declined by 11. Days spent living independently increased by 26, and days spent in congregate or residential settings increased by 47 (p<.001 for each).
Table 3 Days spent in various living situations by 6,584 clients during the 12-month periods before and after enrollment in a full service partnership, by level of program fidelity to the Housing First model
 PreenrollmentPostenrollmentPre-post difference 
Living situationM95% CIM95% CIM95% CIpa
All programs       
 Homeless7168 to 742422 to 26–47–50 to –44<.001
 Emergency shelter3229 to 343331 to 351–2 to 3.309
 Justice system2221 to 241211 to 13–10–12 to –8<.001
 Apartment or single-room occupancy (SRO) hotel8379 to 86109105 to 1132623 to 30<.001
 Congregate or residential6865 to 71115111 to 1184744 to 50<.001
 Parents or family6259 to 655148 to 54–11–13 to –8<.001
 Other or unknown1311 to 141614 to 1732 to 5<.001
Low fidelity (N=1,245)       
 Homeless4640 to 521713 to 20–29–34 to –24<.001
 Emergency shelter3227 to 362823 to 32–4–8 to 1.084
 Justice system3833 to 421714 to 20–21–26 to –16<.001
 Apartment or SRO hotel8375 to 917467 to 82–9–14 to –3.003
 Congregate or residential7164 to 78137128 to 1456558 to 73<.001
 Parents or other family7364 to 805750 to 64–16–21 to –11<.001
 Other or unknown2319 to 262117 to 25–2–7 to 3.430
Midfidelity (N=3,481)       
 Homeless6561 to 692522 to 28–40–43 to –36<.001
 Emergency shelter2724 to 293028 to 3331 to 6.020
 Justice system2220 to 241211 to 14–10–12 to –8<.001
 Apartment or SRO hotel8277 to 869489 to 99128 to 16<.001
 Congregate or residential7672 to 81117112 to 1234136 to 45<.001
 Parents or other family6459 to 685349 to 57–11–14 to –8<.001
 Other or unknown2927 to 322320 to 25–6–9 to –3<.001
High fidelity (N=1,858)       
 Homeless10194 to 1082421 to 27–77–83 to –71<.001
 Emergency shelter4036 to 443733 to 41–3–8 to 1.154
 Justice system119 to 1465 to 8–5–7 to –3<.001
 Apartment or SRO hotel8477 to 90150143 to 1586760 to 74<.001
 Congregate or residential4844 to 535879 to 913731 to 42<.001
 Parents or other family5146 to 563834 to 43–12–16 to –8<.001
 Other or unknown3026 to 331613 to 18–14–18 to 10<.001
a
Estimated by using paired t tests
There were important differences in days spent in residential settings by level of fidelity. Notably, the mean number of days spent homeless in the preenrollment period was lowest at low-fidelity programs, followed by midfidelity and high-fidelity programs (46, 65, and 101 days, respectively). Declines in days spent homeless were also lowest at the low-fidelity programs compared with midfidelity and high-fidelity programs (declines of 29, 40, and 77 days, respectively). Conversely, the number of days spent in justice system settings in the preenrollment period was highest at low-fidelity versus midfidelity and high-fidelity programs (38, 22, and 11 days, respectively), and declines in justice days were greatest among low-fidelity versus midfidelity and high-fidelity programs (declines of 21, ten, and five days, respectively). The number of days spent living in apartments was nearly identical across program types during the preenrollment period, but during the postenrollment period, it increased by 67 days at high-fidelity programs compared with an increase of 12 days at midfidelity programs and a decrease of nine days at low-fidelity programs.
Table 4 shows annual changes in days spent in various residential settings by level of program fidelity, adjusted for individual characteristics of FSP clients. The number of days spent homeless declined at all programs, but the declines were greatest at high-fidelity FSPs compared with midfidelity and low-fidelity FSPs (declines of 87, 49, and 34 days, respectively; p<.001). The number of days spent living independently in an apartment or SRO hotel increased by 44 at high-fidelity FSPs but declined by 21 at low-fidelity programs (p<.001).
Table 4 Estimated changes in days spent in various living situations by 6,584 clients after enrollment in an FSP, by level of program fidelity to the Housing First model, after adjustment for client characteristicsa
 Low fidelityMid-fidelityHigh fidelity 
Living situationM95%CIM95% CIM95% CIp
Homeless–34–55 to –13–49–64 to –34–87–109 to –64<.001
Emergency shelter–1.9–15 to 113–8 to 132–11 to 15.782
Justice system–18–29 to –6–14–22 to –5–10–22 to 2.572
Apartment or single-room occupancy hotel–21–43 to 25–12 to 214420 to 68<.001
Congregate or residential4727 to 683620 to 513312 to 55.466
Parents or other family–18–28 to –8–14–23 to –6–14–24 to 4.612
Other or unknown–10–21 to 2–14–23 to –5–19–31 to –8.274
a
Changes in days spent in each residential setting between the 12 months before and after enrollment in a full service partnership (FSP) were estimated by using generalized estimating equations (GEEs). Each GEE was specified with a Gaussian distribution, identity link, an exchangeable correlation matrix to account for correlated errors within FSP programs, and an exposure offset to adjust for differences in the postenrollment period. Additional control covariates included age, gender, race-ethnicity, clinical diagnosis, diagnosis of a substance use disorder, and Medicaid coverage. Columns do not sum to 0 because of the use of GEEs as separate regressions for each residential setting.
Table 5 shows annual changes in days spent in various residential settings by level of program fidelity, adjusted for individual characteristics and days in the residential setting during the preenrollment period. The difference between the programs in the number of days homeless narrowed considerably (declines of 63 days, high fidelity; 52 days, midfidelity; and 53 days, low fidelity; p=.039). The number of days spent living independently in an apartment or SRO hotel increased by 33 at the high-fidelity FSPs but declined by 30 at the low-fidelity programs (p<.001).
Table 5 Estimated changes in days spent in various living situations by 6,584 clients after enrollment in an FSP, by level of program fidelity to the Housing First model, after adjustment for client characteristics and days spent in the residential setting during the preenrollment perioda
 Low fidelityMidfidelityHigh fidelity 
Living situationM95% CIM95% CIM95% CIp
Homeless–53–63 to –43–52–58 to –44–63–73 to –53.039
Emergency shelter–9–23 to 4–4–14 to 64–10 to 18.312
Justice system–5–12 to 2–8–14 to –3–13–20 to –7.090
Apartment or single-room occupancy hotel–30–51 to –10–8–23 to 73311 to 55<.001
Congregate or residential5435 to 754025 to 55276 to 48.097
Parents or other family–8–19 to 2–8–16 to 0–12–22 to –2.629
Other or unknown–9–19 to 0–11–18 to –3–16–26 to –7.373
a
Changes in days spent in each residential setting between the 12 months before and after enrollment in a full service partnership (FSP) were estimated by using generalized estimating equations (GEEs). Each GEE was specified with a Gaussian distribution, identity link, an exchangeable correlation matrix to account for correlated errors within FSP programs, and an exposure offset to adjust for differences in enrollment in the postenrollment period. Additional control covariates included days in the residential setting in the preenrollment period, age, gender, race-ethnicity, clinical diagnosis, diagnosis of a substance use disorder, and Medicaid coverage. Columns do not sum to 0 because of the use of GEEs as separate regressions for each residential setting.
We conducted a post hoc analysis to investigate the finding that clients at low-fidelity FSPs spent more days in jail during the preenrollment period compared with clients at midfidelity and high-fidelity FSPs. This finding resulted from a single FSP (N=201) that was oriented toward adults exiting the justice system. This FSP had low fidelity (offering mostly emergency, short-term, or transitional housing with readiness requirements and requiring participation in treatment), and its clients spent a high number of days (93±115 days) in the justice system in the year prior to enrollment and had large reductions in days (decline of 71±119 days) in the justice system during the year postenrollment.

Discussion

This study examined the relationship between fidelity to the Housing First model and residential outcomes among clients of supported housing programs in California. We found that clients of high-fidelity FSP programs had longer histories of homelessness compared with clients of low-fidelity FSP programs and that high-fidelity FSP programs placed most of these individuals in apartments or SRO hotels. In contrast, placements by low-fidelity FSP programs were concentrated in congregate and residential settings. There were differences between high- and low-fidelity FSPs in terms of client choice in housing and the degree to which client-driven goals were considered in determining housing placement.
This study had several limitations. Fidelity was measured by using a self-administered survey. This approach offered an expeditious way of obtaining information on a critical array of practices across a wide range of programs but lacked some depth and detail in measurement compared with site visits. Participation in the survey was voluntary, and not all FSPs participated. Residential outcomes were measured after one year in the programs, which may not be enough time to capture the full impact of fidelity on outcomes. The use of an administrative data system could have missed some transitions.

Conclusions

The differences between high- and low-fidelity FSPs are important considering the recent emphasis on consumer self-determination and the movement toward less restrictive and more recovery-enabling housing. Consumer choice, particularly with respect to housing decisions, has been associated with greater satisfaction with housing and quality of life (23,24). Apartments and SRO hotels have been associated with higher independence and functioning compared with more supervised and congregate residences (25,26), although improving social integration remains a challenge (27). Nevertheless, the time spent in congregate and residential settings demonstrates that many programs in this study continued to rely on more traditional supervised and structured accommodations.

Acknowledgments and disclosures

This work was funded though the American Recovery and Reinvestment Act of 2009 by an award from the Agency for Health Care Research and Quality for health care delivery systems research (1R01HS019986).
The authors report no competing interests.

References

1.
Tanzman B: An overview of surveys of mental health consumers’ preferences for housing and support services. Hospital and Community Psychiatry 44:450–455, 1993
2.
Nelson G, Hall GB, Forchuk C: Current and preferred housing of psychiatric consumers/survivors. Canadian Journal of Community Mental Health 22:5–19, 2003
3.
Allen M: Waking Rip van Winkle: why developments in the last 20 years should teach the mental health system not to use housing as a tool of coercion. Behavioral Sciences and the Law 21:503–521, 2003
4.
Transforming Housing for People With Psychiatric Disabilities Report. HHS pub no 4173. Rockville, Md, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services, 2006
5.
O’Hara A: Housing for people with mental illness: update of a report to the President’s New Freedom Commission. Psychiatric Services 58:907–913, 2007
6.
Tsemberis S: The Pathways Model to End Homelessness for People With Mental Illness and Addiction Manual. Center City, Minn, Hazelden, 2010
7.
Cashin C, Scheffler R, Felton M, et al: Transformation of the California mental health system: stakeholder-driven planning as a transformational activity. Psychiatric Services 59:1107–1114, 2008
8.
Greenwood RM, Schaefer-McDaniel NJ, Winkel G, et al: Decreasing psychiatric symptoms by increasing choice in services for adults with histories of homelessness. American Journal of Community Psychology 36:223–238, 2005
9.
Barrow S, McMullin L, Tripp J, et al: Consumer Integration and Self-Determination in Homelessness Research, Policy, Planning, and Services. New York, New York State Psychiatric Institute, 2007
10.
Mares A, Rosenheck R: HUD/HHS/VA Collaborative Initiative to Help End Chronic Homelessness: National Performance Outcomes Assessment Preliminary Client Outcomes Report. West Haven, Conn, Northeast Program Evaluation Center, 2007
11.
Scheffler RM, Adams N. Millionaires and mental health: Proposition 63 in California. Health Affairs, 2005; doi
12.
Gilmer TP, Stefancic A, Ettner SL, et al: Effect of full-service partnerships on homelessness, use and costs of mental health services, and quality of life among adults with serious mental illness. Archives of General Psychiatry 67:645–652, 2010
13.
Gilmer TP, Katz ML, Stefancic A, et al: Variation in the implementation of California’s full service partnerships for persons with serious mental illness. Health Services Research 48:2245–2267, 2013
14.
Salyers MP, Tsemberis S: ACT and recovery: integrating evidence-based practice and recovery orientation on assertive community treatment teams. Community Mental Health Journal 43:619–641, 2007
15.
Tsemberis S: From streets to homes: an innovative approach to supported housing for homeless adults with psychiatric disabilities. Journal of Community Psychiatry. 27:225–241, 1999
16.
Gilmer TP, Stefancic A, Sklar M, et al: Development and validation of a Housing First fidelity survey. Psychiatric Services 64:911–914, 2013
17.
Johnson N, Kotz S, Balakrishnan N: Continuous Univariate Distributions, 2nd ed. New York, Wiley, 1994
18.
StataCorp: Stata Statistical Software: Release 12. College Station, Tex, StataCorp LP, 2011
19.
Patton MQ: Qualitative Research and Evaluation Methods, 3rd ed. Thousand Oaks, Calif, Sage, 2002
20.
Palinkas LA, Horwitz SM, Chamberlain P, et al: Mixed-methods designs in mental health services research: a review. Psychiatric Services 62:255–263, 2011
21.
Crabtree BF, Miller WL: Doing Qualitative Research. Newbury Park, Calif, Sage, 1992
22.
Miles MB, Huberman AM: Qualitative Data Analysis: An Expanded Sourcebook, 2nd ed. Thousand Oaks, Calif, Sage, 1994
23.
Nelson G, Sylvestre J, Aubry T, et al: Housing choice and control, housing quality, and control over professional support as contributors to the subjective quality of life and community adaptation of people with severe mental illness. Administration and Policy in Mental Health and Mental Health Services Research 34:89–100, 2007
24.
Srebnik D, Livingston J, Gordon L, et al: Housing choice and community success for individuals with serious and persistent mental illness. Community Mental Health Journal 31:139–152, 1995
25.
Nelson G, Hall GB, Walsh-Bowers R: The relationship between housing characteristics, emotional well-being and the personal empowerment of psychiatric consumer/survivors. Community Mental Health Journal 34:57–69, 1998
26.
Siegel CE, Samuels J, Tang DI, et al: Tenant outcomes in supported housing and community residences in New York City. Psychiatric Services 57:982–991, 2006
27.
Tsai J, Mares AS, Rosenheck RA: Does housing chronically homeless adults lead to social integration? Psychiatric Services 63:427–434, 2012

Information & Authors

Information

Published In

Go to Psychiatric Services
Go to Psychiatric Services

Cover: Woman Sitting on Couch Looking at Picture, by Alice Barber Stephens. Library of Congress, Prints and Photographs Division, DLC/PP-1933:0012.

Psychiatric Services
Pages: 1311 - 1317
PubMed: 25022911

History

Published ahead of print: 31 October 2014
Published online: 1 November 2014
Published in print: November 01, 2014

Authors

Details

Todd P. Gilmer, Ph.D.
Dr. Gilmer with the Department of Family and Preventive Medicine, University of California, San Diego (e-mail: [email protected]). Ms. Stefancic and Dr. Tsemberis are with Pathways to Housing, Inc., New York City, and Ms. Stefancic is also with the Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, also in New York City. Dr. Katz is with the U.S. Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles. Ms. Sklar is with the San Diego State University–University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego. Dr. Palinkas is with the School of Social Work, University of Southern California, Los Angeles.
Ana Stefancic, M.A.
Dr. Gilmer with the Department of Family and Preventive Medicine, University of California, San Diego (e-mail: [email protected]). Ms. Stefancic and Dr. Tsemberis are with Pathways to Housing, Inc., New York City, and Ms. Stefancic is also with the Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, also in New York City. Dr. Katz is with the U.S. Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles. Ms. Sklar is with the San Diego State University–University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego. Dr. Palinkas is with the School of Social Work, University of Southern California, Los Angeles.
Marian L. Katz, Ph.D.
Dr. Gilmer with the Department of Family and Preventive Medicine, University of California, San Diego (e-mail: [email protected]). Ms. Stefancic and Dr. Tsemberis are with Pathways to Housing, Inc., New York City, and Ms. Stefancic is also with the Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, also in New York City. Dr. Katz is with the U.S. Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles. Ms. Sklar is with the San Diego State University–University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego. Dr. Palinkas is with the School of Social Work, University of Southern California, Los Angeles.
Marisa Sklar, M.S.
Dr. Gilmer with the Department of Family and Preventive Medicine, University of California, San Diego (e-mail: [email protected]). Ms. Stefancic and Dr. Tsemberis are with Pathways to Housing, Inc., New York City, and Ms. Stefancic is also with the Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, also in New York City. Dr. Katz is with the U.S. Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles. Ms. Sklar is with the San Diego State University–University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego. Dr. Palinkas is with the School of Social Work, University of Southern California, Los Angeles.
Sam Tsemberis, Ph.D.
Dr. Gilmer with the Department of Family and Preventive Medicine, University of California, San Diego (e-mail: [email protected]). Ms. Stefancic and Dr. Tsemberis are with Pathways to Housing, Inc., New York City, and Ms. Stefancic is also with the Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, also in New York City. Dr. Katz is with the U.S. Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles. Ms. Sklar is with the San Diego State University–University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego. Dr. Palinkas is with the School of Social Work, University of Southern California, Los Angeles.
Lawrence A. Palinkas, Ph.D.
Dr. Gilmer with the Department of Family and Preventive Medicine, University of California, San Diego (e-mail: [email protected]). Ms. Stefancic and Dr. Tsemberis are with Pathways to Housing, Inc., New York City, and Ms. Stefancic is also with the Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, also in New York City. Dr. Katz is with the U.S. Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles. Ms. Sklar is with the San Diego State University–University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego. Dr. Palinkas is with the School of Social Work, University of Southern California, Los Angeles.

Funding Information

Agency for Healthcare Research and Quality10.13039/100000133: 1R01HS019986

Metrics & Citations

Metrics

Citations

Export Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Format
Citation style
Style
Copy to clipboard

View Options

View options

PDF/EPUB

View PDF/EPUB

Login options

Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.

Personal login Institutional Login Open Athens login
Purchase Options

Purchase this article to access the full text.

PPV Articles - Psychiatric Services

PPV Articles - Psychiatric Services

Not a subscriber?

Subscribe Now / Learn More

PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

Media

Figures

Other

Tables

Share

Share

Share article link

Share