Abstract
Digital technologies provide people new means for exchanging social support, though the extent to which disadvantaged populations benefit is unclear. Youth experiencing homelessness (YEH) are a vulnerable population with a range of support needs who are active digial media users. We examined the core networks of 621 YEH in the United States to assess how digital-only communication compared with any other form of contact in terms of the social support youth reported receiving. Participants were more likely to receive emotional support, as well as find role models and support for their personal goals, though not material support, through contacts that were digital-only (internet, phone, or social media) in the last month. YEH appear to rely on—and in some cases prefer—digital-only communication to tap into resources for survival and advancement. We discuss the implications for research on ICT use by disadvantaged populations and for interventions to support YEH.
How well can disadvantaged groups mitigate the inequalities they experience offline through the use of information and communication technologies (ICTs)? Much research has examined how social support, or the assistance people provide one another, flows through ICTs (see Rains and Wright, 2016 for overview). Disadvantaged communities may benefit uniquely from an ability to access social resources through the use of ICTs (Mesch, 2012; Rains and Tsetsi, 2017). Youth experiencing homelessness (YEH) are a vulnerable population who turn to phones, instant message, social media, and other forms of digital communication to maintain and expand their social networks in the midst of significant need (Humphry, 2021; Rice and Barman-Adhikari, 2014). Yet, the relationship of digital communication to social support for such a population remains unclear. Our knowledge is particularly limited around the range of support types that might be exchanged through digital communication for vulnerable groups, as well as the extent to which digital-only communication compares with ongoing face-to-face relationships in this context.
To explore these questions, we examined social support and digital communication in the core networks of over 600 YEH in the Los Angeles area. We compare support received through relationships maintained solely through digital channels in the previous month with relationships that had a face-to-face component. We find that youth are more likely to locate several forms of social support through relationships maintained solely through digital channels, whether by phone, social media, or the Internet more broadly. Our findings suggest that digital communication not only supplements offline social support, but facilitates such support in a unique way for a population of highly disadvantaged young people. We discuss the contributions of the study to existing research around the role of ICTs in social support for marginalized populations. First, we examine the gaps in our knowledge of these areas, starting with social support for this population.
Social support for disadvantaged youth
Social support, or the assistance people provide one another, is a key contributor to individual health and wellbeing (Cohen and Hoberman, 1983; House et al., 1988; Thoits, 2011). People turn to one another to manage emotional and practical difficulties in life, such as feelings of depression or loneliness, the death of a loved one, or the loss of a job. Cutrona and Suhr (1992) suggest five types of support, including tangible (e.g. providing money), informational (e.g. advice or feedback), emotional (expressing concern), esteem (e.g. confirming another’s abilities), and network support (e.g. feeling one belongs to a group of similar concerns) (Cutrona and Suhr, 1992). The “optimal-matching” theory of social support suggests that such assistance is primarily effective when it matches the needs of the individual and situation (Cutrona and Russell, 1990).
Social support is not only an individual or interpersonal process but is also shaped by one’s position (and the position of one’s network) in society. Studying youth from low-income neighborhoods in the United States and building on social capital theory (Granovetter, 1973; Putnam, 2000), Briggs (1998) highlights the role of “coping” (or survival) and “leverage” (or advancement) support that youth living in poverty receive from their networks. Coping resources, such as emotional and material assistance, from family and friends allow youth to “get by” and survive day-to-day, while social leverage tends to come from outside close friends and family, such as from mentors in the middle class who can offer “school advice, job information, [and] advice on future plans” (Briggs, 1998; Rhodes et al., 2006; Tolan et al., 2014). Locating support of different kinds from different connections is not unique to disadvantaged groups. People tend to access emotional support from strong ties and valuable information from weaker ties, for example (Granovetter, 1973; Putnam, 2000). For those disadvantaged by socioeconomic status and other inequalities, social support may have higher stakes by standing in for resources, such as therapy or tutoring that money can afford for the more privileged (Briggs, 1998; Desmond, 2012; Dominguez and Watkins, 2003).
Role models illustrate a relationship type with unique significance when turning attention to disadvantaged youth. Role models can be defined as people that youth look up to for observed characteristics (Hurd et al., 2009). A number of studies point to the benefits of role models for mental wellbeing and for youths’ healthy life choices (Bird et al., 2012; Bryant and Zimmerman, 2003; Hurd et al., 2009). Programs, such as “Big Brothers Big Sisters,” connect youth with community members and others who serve as role models (Bryant and Zimmerman, 2003). Youth also look up to family members as role models, and research links parental role models with better academic performance and lower mental distress (Bryant and Zimmerman, 2003).
Briggs’ (1998) typology of survival and advancement support has been extended in subsequent inequality research, particularly around social mobility for the children of low-income families in the urban United States (Bryant and Zimmerman, 2003; Dominguez and Watkins, 2003; Gofen, 2009). Few studies around digital social support have focused on these more unique aspects of the networks of disadvantaged youth or adults, with some work referring to Briggs’ (1998) typology in the context of the Internet’s role in local community (Kharisma and Remi, 2020; London et al., 2010). We highlight these gaps further by turning to existing research on ICT use, social support, and social disadvantage.
Digital mediation of social support for disadvantaged populations
With the rapid diffusion of digital technologies in the 21st century, more options are available for people to support each other. Indeed, research over the last decade shows a generally positive association between ICT use and feelings of being supported (Hampton et al., 2011; Liu et al., 2018; Rains and Tsetsi, 2017). The relationship between ICT use and social support is not automatic, however, and depends on how people seek out support of different kinds through various relationships and communication channels (Liu et al., 2018; Rains and Tsetsi, 2017). For example, studies of Facebook indicate that people can benefit in their access to information and advice over the platform, but less so in terms of additional emotional or material support (Li et al., 2015; Trepte et al., 2015; Utz and Breuer, 2017).
Social inequality is an important, though less explored, factor in the digital mediation of social support. Disadvantaged populations—such as those marginalized by income, education, race or ethnicity, and sexuality or gender identity—may stand to gain uniquely from the support potential of digital technologies (Craig et al., 2021; Gonzales, 2017; Rains and Tsetsi, 2017). For example, some research suggests that while people of lower socioeconomic status use ICTs to diversify their social networks, such as up the income or education ladder, privileged individuals are more likely to maintain already diverse networks through ICTs (Gonzales, 2017; Mesch, 2012). In other contexts, people with rare health conditions or stigmatized identities may seek out groups of supportive strangers online for advice and esteem (Austin et al., 2020; Barak et al., 2008; Craig et al., 2021).
For individuals lacking more critically in basic needs, such as those lacking stable shelter, translating ICT use into novel social advantages may be both more difficult and more beneficial if successful (Calvo and Carbonell, 2019; Marler, 2022). In an ethnographic study of adults experiencing homelessness, Marler (2022) found that attempts to tap into social support over social media and crowdfunding platforms were largely unsuccessful among people living without stable shelter, due to overlapping inequalities of access, skills, and offline social support. Young people experiencing homelessness may have different experiences than adults, however. YEH have relatively high rates of technology adoption and a wide range of uses, in a way that may better prepare them to benefit from the support opportunities of digital channels, such as support via text or social media (Hammond et al., 2018; Rice and Barman- Adhikari, 2014; Rice et al., 2012). However, there is little research exploring how digital communication shapes social support opportunities for young people in such circumstances, which the focus of the following section.
Digitally mediated support for YEH
YEH are “individuals, between ages 13 and 25, living in places not meant for human habitation, in shelters or transitional housing (or other temporary housing arrangement), or staying with others while lacking a safe and stable alternative living arrangement” (Morton et al., 2018). An estimated 1–3 million youth experience homelessness each year in the United States (Morton et al., 2018). A range of negative health outcomes are associated with homelessness among youth, including physical violence, substance abuse, post-traumatic stress disorder (PTSD), and sexually transmitted diseases (STDs) (Bender et al., 2015; Fernandes-Alcantara, 2019). YEH who report higher levels of social support tend to report lower levels of these repercussions, highlighting the importance of interpersonal assistance in this population (Barman-Adhikari et al., 2016).
Types of support and digital communication for YEH
Support of different kinds is likely to be more and less accessible to youth over digital channels though existing studies provide only partial guidance in this regard. The overall associations between relationships and support are complex and explored in detail (see Barman-Adhikari et al., 2016; De la Haye et al., 2012). However, several findings can be highlighted in relation to digital communication. An important distinction providing context for these findings is between the “home-based” and “street-based” ties that youth develop as they transition to the street. Digital communication tends to be more prominent with “home-based” ties of family and friends from before becoming homeless, and less prevalent with “street-based” contacts met since, including other unhoused youth and staff of social agencies (Rice and Barman-Adhikari, 2014; Rice et al., 2012). Street-based relationships are shown to be maintained more face-to-face, in line with youth’s physical transition to the street (Rice and Barman-Adhikari, 2014), as explored in greater depth in the following section. Ties to street peers tend to associate with negative wellbeing outcomes, such as riskier behaviors and higher likelihood of depression, while relationships with family and other home-based ties are more often protective of these risks (Rice, 2010; Rice et al., 2012; Wenzel et al., 2010).
Research on social support outcomes, as opposed to these other wellbeing measures, tends to be more limited, particularly when it comes to the role of digital communication. In studies comparing support received across type of social tie, family members are found to be more likely to provide YEH with emotional (Barman-Adhikari et al., 2016; De la Haye et al., 2012) and tangible support (De la Haye et al., 2012) as compared to other ties, while street peers are less likely to do so. However, research also suggests that youth with a larger proportion of street peers in their networks are more likely to receive emotional and tangible support from such peers, suggesting that street peers and not only home-based ties are key supporters for one another under certain conditions (De la Haye et al., 2012). Agency staff, such as social workers, are shown to provide tangible assistance for YEH, though at rates lower than family members (Barman-Adhikari et al., 2016).
Research on social support outcomes of different communication channels for YEH is more limited, making it difficult to ascertain whether youth similarly leverage digital channels for social assistance Particularly when it comes to the role of digital communication. In surveys that examine differences across kinds of social ties, family members are more likely to provide youth emotional (Barman-Adhikari et al., 2016; De la Haye et al., 2012) and tangible support (De la Haye et al., 2012) in comparison to other kinds of connections, while street peers are less likely to provide such support than others. However, youth with a larger proportion of street peers in their networks are more likely to receive both these forms of support from such peers, suggesting that YEH are key supporters for one another under certain conditions (De la Haye et al., 2012). Agency staff are also shown to provide tangible assistance for YEH though at rates lower than family members (Barman-Adhikari et al., 2016).
Our knowledge of how digital communication shapes access to different kinds of support for youth remains especially limited. Existing research suggests that emotional and instrumental support may be more common through family members and home-based friends than with street-based ties (Barman-Adhikari et al., 2016; De la Haye et al., 2012), ties that are also more likely to be sustained with the assistance of digital technologies, such as mobile phones, email, and social media (Rice and Barman-Adhikari, 2014; Rice et al., 2012). However, the relationship between digital communication and social support types has not been tested directly. Street-based ties, such as social workers other unhoused youth, for example, may provide degrees of emotional and instrumental support, though existing research suggests such support may be limited, with face-to-face interaction being more prominent for these relationships (Rice et al., 2012).
The existing research provides even less guidance as to relationships that, in Briggs’ (1998) terms, can help YEH not only survive the street but also “advance” out of homelessness. As discussed above, youth may lean on role models for the latter form of guidance, as well as those who can support their personal goals, such as for their education or career. We can summarize these two forms of advancement aid as “role model” (Bird et al., 2012; Bryant and Zimmerman, 2003) and “personal goals” support (Briggs, 1998), respectively, drawing on the existing literature. Despite the importance of such support for youth’s social advancement, there is little research to guide our understanding of how digital communication plays a role in these forms of assistance.
Digital-only communication and social support for YEH
An additional ambiguity in the existing research on YEH’s support networks is the extent to which forms of digital communication are the sole means of communication with potentially supportive ties, or whether they persist alongside ongoing face-to-face interaction. This is important as YEH have varying degrees of face-to-face interaction with their networks. Some youth live temporarily with kin while remaining unstably housed (Fernandes-Alcantara, 2019), while others travel significant distances from home as part of their transition to the street (Fernandes-Alcantara, 2019; Morton et al., 2018).
A good deal of communication research points to the role of face-to-face interaction in supportive relationships (Baym et al., 2004), as well as to the unique affordances of mediated channels (Gonzales, 2014; Indian and Grieve, 2014; Reid and Reid, 2007). Though face-to-face is often considered the gold standard for interpersonal exchanges, providing more social cues for interaction (Daft and Lengel, 1986; Short et al., 1976), mediated communication may have advantages for interaction quality by better meeting individuals’ preferences for social engagement (Gonzales, 2014; Indian and Grieve, 2014; Walther, 1996). For example, young people as well as others may prefer the ability to control the timing and crafting of messaging through text-based communication as opposed to face-to-face interaction (Reid and Reid, 2007; Walther, 1996). Research shows that people with higher levels of social anxiety may find mediated communication, such as text and social media more conducive to support interactions (Indian and Grieve, 2014; Reid and Reid, 2007), including for youth and their families (Crosswhite et al., 2014).
For YEH, communicating by phone, text, and social media with social network members may be conducive to relationships of support in ways unmatched by face-to-face interaction. Many youth who become homeless have experienced victimization and abuse prior to leaving home (Barman-Adhikari et al., 2016; Fernandes-Alcantara, 2019). Unlike for some other youth, such as those leaving home for college and maintaining contact with friends and family back home over social media platforms (Ellison et al., 2007), for youth whose relationships with family and other members of their support network are more likely to be distressed (Whitbeck and Hoyt, 1999), digital-only communication could be more conducive to exchanging emotional support. YEH may prefer an ability to control the timing, crafting of messages, and extent of communication through digital channels, as evidenced in research with the broader population (Crosswhite et al., 2014; Reid and Reid, 2007; Walther, 1996).
On the other hand, there is reason to suspect that youth will find less support in relationships they keep up with only over digital channels, as opposed to relationships with ongoing face-to-face interaction. Research generally shows that strong ties (those willing to provide key emotional and material support) tend to maintain over several channels, including, importantly, face-to-face interaction (Baym et al., 2004; Haythornthwaite, 2002; Wright et al., 2013). Emotional support from ties (such as supportive text messages, video calls, or social media comments) as well as material support (such as financial aid sent from key network members) may fade over time as relationships sustain only over digital interaction, including for YEH.
People who youth look up to and receive advice from—representing opportunities for advancement (Briggs, 1998)—may require less of an ongoing face-to-face relationship, however. Keeping up through low-cost forms of communication, such as instant text or exchanges on social media may be more conducive in such relationships, as is shown when people exchange information and advice over SNSs (Ellison et al., 2014; Utz and Breuer, 2017). On the other hand, programs to match youth with mentors are more often based in face-to-face interaction (Garringer et al., 2017), suggesting that digital-only communication may be less associated with support for advice on school and career goals. However, research around these support roles is limited in the digital literature, which motivates the present study.
Current study
In this study, we examine the social networks of 621 YEH in Los Angeles to understand how digital-only communication relates to the sources of social support youth report in their close networks. We draw on the distinction provided by Briggs (1998) between “coping” (or survival) and “leverage” (or advancement) resources in the social networks of low-income youth. Acknowledging that youth not only need emotional and material assistance to survive day-to-day, but also relationships that help them to advance off the street, we evaluate the presence of role models and figures in youth’s lives who help them meet their personal goals, respectively, as examples of advancement support. We evaluate the kinds of ties that youth maintain through digital communication as well as the different forms of support that flow through these digital-only ties, irrespective of the nature of the tie.
Research questions
We propose the following research questions:
RQ1: What kinds of ties are more likely to be digital-only within the last month for YEH?
RQ2: What kinds of support that YEH receive—emotional, material, role model, and personal goals—are made more likely by digital-only communication with a given tie?
Methods
The current study utilizes baseline data from Have You Heard? (HYH), a longitudinal study of a peer-led social network intervention to prevent HIV among YEH. Data collection for the current study occurred in 2017 at three drop-in centers in Los Angeles, CA. All youth receiving services were eligible for the study and were approached to participate upon entering the drop-in center. A total of 731 YEH were recruited and all youth were between the ages of 14 and 26 at the time of study enrollment, reflecting the age ranges of youth who commonly sought services at these drop-in centers.
Youth provided informed consent to participate in a self-administered survey, which included an assessment of their sociodemographic characteristics and personal social networks. Youth were compensated US$20 for their participation in the survey. This study only included youth who completed both individual and social network components of the survey, rendering a final sample size of 621. Of this number, 63% identified as homeless, as reported in the results section below. We include all 621 participants for several reasons. First, all participants were recruited when seeking services at a homeless drop-in center, suggesting some form of housing need. Second, research suggests that the actual proportion of YEH tends to be higher than what is self-reported (Winetrobe et al., 2017). Youth who are couch-surfing, for example, often do not identify as homeless to social services, despite falling under standard definitions (Petry et al., 2022).
YEH are a vulnerable population and the data collection involved significant considerations for participant protection. The data collection team was led by a study coordinator with a master’s degree in social work. Research staff were either PhD students in social work or social work students working toward a master’s degree. All staff were trained in sensitivity to marginalized populations, emergency protocols, and the care of private information. All data are stored on encrypted, password-protected cloud storage, while files containing personal identifiers, such as names are saved separately from files that contain survey responses, which are coded only with study identifiers. The amount of reimbursement was determined in line with previous research with this population, with the goal of honoring youth’s participation in the research while minimizing the potential for coercion (e.g. Barman-Adhikari et al., 2016; Rice 2010). All study procedures were approved by the University of Southern California Institutional Review Board.
Measurement
The study’s individual and social network data collection procedure created a multilevel data structure, with nominated network members (i.e. alters; Level 1) nested within the social network of each participant (i.e. ego; Level 2). Therefore, the lower level (Level 1) variables cover alter characteristics (e.g. alter type) and alter–ego relational characteristics (e.g. relationship type); the higher level (Level 2) variables relate to the participant, such as personal characteristics.
Alter/relationship-level variables (Level 1 variables)
Independent variables
Through a social network survey, information was collected about participants’ network members (“alters”) and their relationships with those members. Specifically, this study focused on alter types, alter characteristics, and ego–alter interaction. These measures are used and validated in previous research (Johnson et al., 2005; Martino et al., 2011; Rew et al., 2008; Rice et al., 2007; Wenzel et al., 2012). Participants were prompted in the following way to elicit their network members: “Think about the last month. Who are people that you have interacted with or talked to the most in the past month? This could be face-to-face or over email, text, phone, social media, etc.” Participants were provided a list of examples, including: “Friends from home or from before you were homeless”; “Friends or other peers you know from the street or peers you interact with at this agency”; “Family (could include both biological and foster family)”; “Person you are romantically, intimately or sexually involved with”; “Case worker, social worker, agency staff or volunteer”; “People from School”; and “People from Work.”
Then, a number of questions were asked about each nominated alter. Participants were asked to identify whether the alter was a relative (“Who on this list is family?”), whether they were known before becoming homeless or unstably housed (hereon, “home-based” ties; Rice et al., 2007), or whether they were met since (hereon, “street-based” ties; Rice et al., 2007), and whether the alter was a service provider (with examples being “case manager, agency staff, or volunteer”). Dichotomous variables were derived with one depicting each of the alter types. Participants were asked to report or approximate if the alter was also a young person (dichotomous variable with 1 = perceived the alter as between the ages of 14 and 25), their gender (nominal variable, with 1 = female, 2 = male, and 3 = transgender), sexual orientation (dichotomous variable with 1 = perceived as lesbian, gay, bisexual or questioning, and 0 = heterosexual), and homeless status (dichotomous variable with 1 = perceived alter “has unstable housing, is homeless, or lives in a shelter,” and 0 = not). These alter types and characteristics have been suggested to be critical in previous literature in shaping YEH’s norms and behaviors (Barman-Adhikari et al., 2015; Wenzel et al., 2012). Finally, for ego–alter interaction, the survey asked about the frequency of the participant’s communication with the alter, which is a dichotomous variable with 1 = participant talks to alter at least once a week (face to face, phone, or online) and 0 = not.
Participant-level variables (Level 2 variables)
Independent variables
The participants’ personal characteristics selected as independent variables included homeless identity (dichotomous variable with 1 = self-identified as homeless and 0 = did not self-identify as homeless; Winetrobe et al., 2017), “traveler” identity (dichotomous variable with 1 = self-identified as a traveler and 0 = did not self-identify as a traveler; Martino et al., 2011), duration of homelessness over lifetime (dichotomous variable with 1 = had experienced homelessness at least a year in lifetime and 0 = had experienced homelessness for less than a year; Rew et al., 2008), and transiency (dichotomous variable with 1 = had moved to different cities at least twice in the past year and 0 = had not moved to different cities at least twice in the past year; Martino et al., 2011). As described below, all independent variables not showing a significant association with outcomes in bivariate analyses were excluded from the final multivariate models.
Demographic controls
A number of sociodemographic characteristics are included as controls. Age is measured as a continuous variable. Race and ethnicity is measured as a nominal variable with 1 = White; 2 = Black; 3 = Latinx; and 4 = multi-racial or other. “Other” combines Asian (n = 8 or 1%) and Native and Pacific Islander (n = 27 or 4%) respondents. Other controls include gender (nominal variable; 1 = cisgender male; 2 = cisgender female; and 3 = gender minorities, including transgender male, transgender female, gender queer, gender non-conforming, and other identities), sexual orientation (dichotomous variable; 1 = sexual minority, including gay or lesbian, bisexual, questioning or unsure, asexual, and others; 0 = heterosexual), and education attainment (dichotomous variable; 1 = had at least high school or general equivalency diploma [GED] degree; 0 = did not have high school or GED degree) were included to serve as control variables.
Dependent variables
For RQ1, the outcome of interest is digital-only communication with nominated network members. Participants were presented with their list of network members and asked with whom they had interacted “ONLY over the phone or internet or social media in the last month.” A dichotomous variable was derived with 1 = digital-only tie and 0 = not a digital-only tie. The measure of digital-only communication is adapted from previous research in which YEH were surveyed on their communication with supportive networks (Rice, 2010; Rice and Barman-Adhikari, 2014). Our measure provides broad categories of digital communication to capture the range of ways that young people may connect digitally.
For RQ2, the outcome of interest is social support from alters. Emotional support is represented by: “In the last month, when you have been in crisis, feeling depressed or dealing with drama and major issues, who have you gone to for help or advice?” Material support is represented by: “In the last month, who have you borrowed money or other material things from when you needed it?.” In terms of advancement support, personal goals support is represented by: “Which of these people support you to meet your personal goals (school, work, etc.)?” Role model support is represented by: “Which of these people would you say you look up to or consider a role model?.” Following the procedure described previously, four dichotomous variables were derived with 1 = had provided such support to participants in the past month and 0 = had not provided such support to participants in the past month.
Analysis
To answer our research questions, we used a multilevel modeling approach to examine the correlates of digital-only communication with participants’ relationships (RQ1) and support sources (RQ2). Also known as dyadic analysis in social network research (de la Haye et al., 2012; Snijders et al., 1995), the multilevel modeling approach is appropriate to the one-to-many structure of the data set in this study. That is, each respondent in the study named several network members and the participant–alter dyads that result are nested within the set of such dyads (up to 5) for a given participant, and thus are not independent in the data structure.
For RQ1, we examined the associations between individual and relational characteristics (our independent variables) and digital-only communication (our dependent variable). Individual-level variables were the characteristics of the respondent (e.g. respondent demographics) while variables at the relational level include characteristics of the alter (e.g. alter demographics) and of the alter–respondent relationship (e.g. whether the alter was a family member). For RQ2, we examined the associations between digital-only communication (our independent variable) and forms of support (our dependent variables).
We first conducted bivariate multilevel analysis to examine the associations between each of the independent variables with the outcomes of interest. All independent variables that were found to be significant (p < .05) in the bivariate analysis were included in the final multivariate multilevel models. Demographic variables were included in the final models across all outcomes of interest, regardless of their relationships with outcomes in bivariate analysis.
Findings
Table 1 illustrates descriptive statistics of the participants. The racial composition of participants was diverse, with no single racial or ethnic group comprising over one-third of participants. Black youth and youth identifying as mixed race represented the largest racial groups. Cisgender males represented over 65% of the youth and over 43% identified as non-heterosexual, in line with the higher proportion of sexual minorities generally reported among unhoused youth (McCann and Brown, 2019). Over three-fourths had completed high school or a GED. Among participants, 63% identified as homeless, a point discussed in the “Methods” section above. Close to 58% reported experiencing homelessness for longer than a year in total.
Sociodemographics and ego-alter relationship characteristics.
SD: standard deviation; GED: general equivalency diploma.
As for social network composition, most participants listed the maximum five network members allowed on the survey (M = 4.89; SD = 0.57). A total of 3038 network members were nominated across all participants. Around 20% of nominated network members were family members, 33% were home-based ties, 47% were street-based ties, 10% were service providers, and 34% were others who had also experienced homelessness. Approximately, 54% of the nominated members were considered frequent contacts, that is, interacted with at least once per week.
As for the outcomes of interest, 17% of the nominated network members were those that participants had only communicated with only over phone, Internet, or social media in the last month; over 47% were perceived by participants as members who support them to meet their goals; 29% were members participants had reached out to for help or advice when needed; only 3% were members from whom participants had borrowed money or other materials; and 27% were perceived as role models.
Table 2 demonstrates the results of the final multivariate, multilevel analysis models. The outcomes for RQ1 are found in the column “Digital-Only Communication.” For RQ1, youth were more likely to have relied on digital means to communicate in the last month with relatives (odds ratio [OR] = 2.66; 95% confidence interval [CI] = [1.89, 3.76]) and home-based ties (OR = 2.94; 95% CI = [2.08, 4.13]). They were less likely to have relied on digital means to communicate with street-based ties (OR = 0.54; 95% CI = [0.39, 0.77]) and alters who were homeless (OR = 0.42; 95% CI = [0.30. 0.60]).
Final multilevel multivariate analysis: digital-only communication and support types by individual and alter/relationship factors.
OR: odds ratio; CI: confidence interval; GED: general equivalency diploma.
An odds ratio (OR)= 1 indicates no association, OR > 1 is a positive association, OR between 0 and 1 is a negative association; dashes represent non-significant associations on the corresponding outcome in bivariate analysis.
Reference category is White.
Reference category is cisgender male.
Reference category is cisgender female.
p < .05; **p < .01; ***p < .001.
For RQ2, digital-only network members were more likely to provide emotional support (OR = 1.83; 95% CI = [1.36, 2.46]), support for personal goals (OR = 3.93; 95% CI = [2.70, 5.72]), and to be perceived as role models (OR = 2.37; 95% CI = [1.73, 3.24]). No significant relationship was found between digital-only communication and material support.
Discussion
The goal of this study was to examine whether the use of ICTs allowed for a particularly disadvantaged group—YEH—to sustain social support of different kinds over digital-only communication. First, we assessed the kinds of relationships that were more likely to be maintained without face-to-face communication in the previous month. Communication with family members and other home-based ties were more likely to be digital-only in nature as compared to street-based ties, confirming existing research (Barman- Adhikari et al., 2016; Rice et al., 2012). Then, we analyzed the correlations between digital-only communication and different kinds of social support. Controlling for relationship type, digital-only communication was associated positively with emotional, role model, and personal goals support though not material support. Independent of the relationship type, digital channels appear to offer unique settings for sustaining several kinds of social support for YEH. Our findings offer evidence of the novel opportunities of ICT use for accessing social support for a particularly marginalized group.
The first contribution of the study is to expand on and specify the ways that ICT use can contribute to overcoming offline barriers to social resources. Research offers evidence that within the general population, the use of ICTs offers unique pathways to social resources for those disadvantaged by income, age, and network size (Rains and Tsetsi, 2017); similarly, for racial minorities and those with less education, online communication is more beneficial for crossing socioeconomic lines than for more privileged groups (Gonzales, 2017; Mesch, 2012). In this study, we specify more closely the kinds of relationships that matter in the context of significant socioeconomic disadvantage, that is, advancement as well as survival support, as well as the communication contexts, namely, digital-only communication, in which these relationships are likely to be maintained.
Our findings update and confirm the results of previous research in the social work field (Rice, 2010; Rice and Barman- Adhikari, 2014), which show that youth rely on digital channels especially to maintain home-based networks, including family and friends known before becoming homeless. This is significant as YEH tend to benefit in various wellbeing measures from these connections (Rice et al., 2012). Our measure of digital communication overlaps with measures of digital communication in previous studies—capturing communication by phone as well as online communication broadly (Rice, 2010; Rice et al., 2012). However, in this study, we update our list to include social media, reflecting the increasingly diverse ways YEH keep up with close connections. Our findings illustrate the ways that youth leverage ICTs to connect to relationships in support of their wellbeing in the absence of ongoing face-to-face interaction, as choices for digital communication expand.
Our study further contributes by showing for the first time the range of social support types that digital-only communication facilitates (and is more likely to facilitate) for a particularly disadvantaged group. Our measures of social support capture the emotional and tangible dimensions of Cutrona and Suhr (1992), as well as adapt these measures to address both the survival (emotional and material) and advancement (here, connections to role models and goals-supporters) resources which are crucial in the context of socioeconomic disadvantage (Briggs, 1998). This is important as the ways that disadvantaged groups—here, YEH—seek out and locate support in their social networks are in important respects different from more privileged populations, in ways lacking acknowledgment in previous research.
We found that digital-only relationships in the last month were more likely to supply youth with both the emotional resources to get by day-to-day (survival support), as well as role models and goals support to improve youth’s situation over time (advancement support). Material assistance—as an example of the former, survival support—is the exception. This may be explained by the overall low percentage of participants who reported receiving this form of support from their core networks.
Our study contributes to an understanding how digital communication differs from other forms, such as hybrid and face-to-face, for disadvantaged populations. Our findings suggest that through a range of digital options for communication, YEH are able to better choose the kinds of support interactions they prefer—and avoid interactions that detract from feelings of support—by keeping relationships at a physical distance. Such an explanation is grounded in the optimal matching theory, which argues that social support is primarily effective when the kind of support offered—for example, emotional or tangible—matches that which is being sought (Cutrona and Russell, 1990). YEH may find benefits in disclosing feelings over mediated rather than face-to-face channels (Crosswhite et al., 2014; Reid and Reid, 2007) with family members, role models, and mentors, at least for some time, with our study examining the previous month. Face-to-face interaction may alternatively be less voluntary and less conducive to emotional support exchange for YEH. For example, relying on contacts for car rides or occasional sleeping arrangements may create tensions in relationships with the provider (see Desmond, 2012) that would be avoided in relationships where communication is at a distance.
Future research is needed to explore the role of digital communication in the support networks of disadvantaged youth and similar populations. Our study provides some evidence that through the use of digital communication, YEH like many other young people and adults (Crosswhite et al., 2014; Indian and Grieve, 2014) are able access the kinds of relationships and interactions that make them feel supported, including when those relationships are unavailable face-to-face. Future studies might expand our understanding of the support systems of particularly disadvantaged groups through the lens of the different affordances of computer-mediated channels (Antheunis et al., 2007; Fox and McEwan, 2017). Research can also pay closer attention to the unique ways that support flows in disadvantaged communities, such as through the unique stakes and forms that supportive relationships have for groups marginalized by their status. Such research promises to better account for how disadvantaged groups manage their support needs through an evolving set of digital technologies.
Limitations
There were a number of limitations to our research approach for understanding ICT use and social support among a hard-to-reach population. First, our measures of social support approximated but were not precisely aligned with existing support scales, such as Cutrona and Suhr (1992). Our measure of emotional support may also include aspects of informational support, by asking to whom youth turn for “advice or help.” We do not measure esteem or network support explicitly. Despite these limitations, considering the hard-to-reach nature of this population and limited existing research, this study offers an important first step for understanding how ICT use relates to social assistance broadly for a particularly disadvantaged group. Furthermore, we apply two novel measures of support, for personal goals and role models, which are particularly salient in the case of disadvantaged youth (Briggs, 1998).
Second, our measure of digital communication did not allow us to distinguish between different channels of digital communication and their relationship to social support. Our measure of digital communication asked respondents to consider “phone or internet or social media” and as such our findings may reflect a range of communicative possibilities related to support access. Despite a broad measure of digital communication, our study helps fill the gap in our knowledge around the use of ICTs for social support for a hard-to-reach population. As mentioned above, future research can distinguish between different digital channels for support access among YEH, drawing on concepts, such as social presence (Short et al., 1976) and affordances (Fox and McEwan, 2017) to explore social media, instant message, and other modes separately.
Conclusion and recommendations
Young people who lack stable housing turn to digital technologies to sustain connections of support for their survival and personal advancement. As homelessness disrupts youth’s offline support systems and poses increased risks for health and wellbeing, connections maintained solely over digital means offer youth sources of emotional aid, role models, and support for personal goals. These findings of this study support the notion that digital communication is one avenue by which disadvantaged individuals can mitigate broader inequalities in access to social resources.
Our study has a number of implications for interventions designed to support YEH. Mobile phones and apps are promising avenues for connecting youth to social services amid the transiency of life while homeless (Adkins et al., 2017; Rice et al., 2011). Youth appear highly motivated to participate in mobile-based interventions, such as for mental health, though evidence of clinical improvements is lacking to date (Glover et al., 2019; Schueller et al., 2019). Our findings should provide further encouragement for exploring the digital possibilities for interventions for youth lacking stable housing. Our findings speak to the feasibility of digital approaches over longer time periods and to the potential for mentoring programs. First, while more frequent in-person interactions with youth is an important goal, our findings suggest that 1 month is not an infeasible period for relying on phone- and app-based interventions for some degree of support. Second, in addition to addressing more immediate concerns through therapists and case managers, digital programs might also succeed in connecting youth to mentors and other role models who can help youth pursue their personal goals, such as for employment or schooling (see also Rice and Barman-Adhikari, 2014), beyond day-to-day survival.
Footnotes
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by a grant from the California HIV/AIDS Research Program.
