Abstract: Structural Social Networks and Positive Wellbeing Among the Homeless (Society for Social Work and Research 26th Annual Conference - Social Work Science for Racial, Social, and Political Justice)

642P Structural Social Networks and Positive Wellbeing Among the Homeless

Sunday, January 16, 2022
Marquis BR Salon 6, ML 2 (Marriott Marquis Washington, DC)
* noted as presenting author
Reuben Addo, Phd, Assistant Professor, California State University, Fresno
Johanna Baker, Social Worker, University of Southern Maine, Portland
Randall Nedegaard, PhD, Associate Professor, California State University, Fresno, Fresno, CA
Paige Harris, MSW Student, California State University, Fresno, Fresno, CA
Background/Purpose: Studies have examined the social networks and wellbeing of the homeless from a deficit-oriented perspective, stressing the associations between homeless social networks and negative wellbeing outcomes. This negative image of homeless social networks has had policy and practice implications. However, there are positive aspects of homeless social networks’ structural properties that may contribute to positive wellbeing.Examining structural social network properties and wellbeing from a strengths perspective may inform practice interventions with the homeless population. In contrast to the deficit-oriented approach, this study examines how structural network properties among the homeless predict positive wellbeing.

Method: A cross-sectional survey design was used to collect data. A convenience sample of homeless adults (N=100) was recruited close to a homeless shelter in a Northeastern U.S. city in 2020. Eligible participants were at least 18 years old and currently homeless. Participants’ mean age was 44.82 and 82% were White.

Measures: The outcome measure positive wellbeing was assessed using three questions from the General Wellbeing Schedule (GWB). Items included 1) feeling in general; 2) happy, satisfied with life; and 3) interesting daily life. Items were assessed on a 6-point scale (Cronbach's α = .80)

Independent variables: Structural network properties were assessed using the Social Network Questionnaire (SNQ). The SNQ uses three concentric circles to diagram participants’ social networks. Participants were asked to place those who are “so close that it's hard to imagine life without them” in the inner circle, those who are “not quite as close, but still very important” in the second circle, and those who they have not mentioned “but who are close enough and important enough in their life” in the outermost circle. Frequency of contact was assessed by asking respondents to indicate how often they contacted network members on a 5‐point scale (1 = very rarely and 5 = very frequently). Network size was assessed by the total number of people in the circles.

Covariates included anxiety, depression, and gender. Depression was measured with three questions and anxiety was measured with fives items from the GWB. Gender was self-reported by participants.

Analysis: Bivariate correlations were conducted to assess relationships among variables. T-test was conducted to determine whether positive wellbeing differed by gender. Multiple regression was conducted to determine how frequency of contact and network size predict positive wellbeing, after controlling for gender, anxiety, and depression.

Results: The combination of gender, anxiety, depression, frequency of contact, and network size significantly predicted positive wellbeing, F (5, 88) = 35.42, p <.001. Anxiety, depression, frequency of contact, and network size were all significant. The results indicated 65% of the variance in positive wellbeing was explained by the model.

Implications: The study suggests frequency of contact and network size contribute to positive wellbeing among the sample of homeless adults. In contrast with previous studies, this study highlights the positive contributions of structural network properties to wellbeing among the homeless. Homeless interventions may include expanding network size and increasing frequency of contact with network members.