Background Youth experiencing homelessness face substance use problems disproportionately compared to other youth. A study found that 69% of youth experiencing homelessness meet the criteria for dependence on at least 1 substance, compared to 1.8% for all US adolescents. In addition, they experience major structural and social inequalities, which further undermine their ability to receive the care they need. Objective The goal of this study was to develop a machine learning–based framework that uses the social media content (posts and interactions) of youth experiencing homelessness to predict their substance use behaviors (ie, the probability of using marijuana). With this framework, social workers and care providers can identify and reach out to youth experiencing homelessness who are at a higher risk of substance use. Methods We recruited 133 young people experiencing homelessness at a nonprofit organization located in a city in the western United States. After obtaining their consent, we collected the participants’ social media conversations for the past year before they were recruited, and we asked the participants to complete a survey on their demographic information, health conditions, sexual behaviors, and substance use behaviors. Building on the social sharing of emotions theory and social support theory, we identified important features that can potentially predict substance use. Then, we used natural language processing techniques to extract such features from social media conversations and reactions and built a series of machine learning models to predict participants’ marijuana use. Results We evaluated our models based on their predictive performance as well as their conformity with measures of fairness. Without predictive features from survey information, which may introduce sex and racial biases, our machine learning models can reach an area under the curve of 0.72 and an accuracy of 0.81 using only social media data when predicting marijuana use. We also evaluated the false-positive rate for each sex and age segment. Conclusions We showed that textual interactions among youth experiencing homelessness and their friends on social media can serve as a powerful resource to predict their substance use. The framework we developed allows care providers to allocate resources efficiently to youth experiencing homelessness in the greatest need while costing minimal overhead. It can be extended to analyze and predict other health-related behaviors and conditions observed in this vulnerable community.

No existing conceptual framework can describe the family precursors and dynamics that explain East Asian American (EAA) youth's mental health problems in their cultural context. To address this conceptual gap and better elucidate EAA youth's mental health issues, we present a conceptual framework that combines two existing theories: the triadic model of family process and acculturation gap-distress theory. We selected these two theories because they address youth mental health problems as a common outcome explained by family domains and complement each other by addressing certain processes in more depth. Using a cultural lens that allows researchers to reconstruct assumptions of theory in specific cultural contexts, the presented integrative framework of family processes identifies how universal and East Asian-specific aspects of familial factors influence the mental health problems of EAA youth. This framework is intended to guide future research efforts on EAA youth development and provide preliminary guidelines for social workers working with EAA families to provide culturally responsive care and services.

Youth experiencing homelessness (YEH) face elevated risks of HIV and STIs compared to their housed counterparts. HIV and STI testing services are pivotal for prevention and early detection. Investigating utilization rates and associated factors among YEH provides critical insights for intervention efforts in major U.S. regions. This study analyzed secondary data from the Homeless Youth Risk and Resilience Survey (HYRRS) conducted between 2016 and 2017. Participants were recruited in seven major cities: Los Angeles, San Jose, Phoenix, St. Louis, Denver, Houston, and New York City (n = 1426). Notably, YEH in Denver, Houston, Phoenix, San Jose, and St. Louis were significantly less likely to use HIV testing services than those in Los Angeles. YEH reporting early sexual activity were less likely to undergo HIV testing, while having online sex partners increased the likelihood of HIV testing. Moreover, YEH in New York City were more likely to receive STI testing, while Phoenix and San Jose had lower testing rates. Disparities in testing rates highlight questions about equitable resource allocation, accentuating the need for enhanced educational and community outreach efforts to address barriers across diverse urban settings.

Purpose Sexual and gender minority (SGM) young adults are disproportionately impacted by homelessness and heavy drinking (i.e., having five or more drinks of alcohol in a row within a couple of hours). Social support, in general, is protective in reducing individuals’ risk of heavy drinking. However, whether and how support from different sources may have different implications on heavy drinking among SGM young adults experiencing homelessness (SGM-YAEH) remains unclear. Informed by the risk amplification and abatement model (RAAM), this study examined the associations between support sources and heavy drinking among SGM-YAEH. Methods A purposive sample of SGM-YAEH (N=425) recruited in homeless service agencies from seven major cities in the U.S. completed a self-administered computer-assisted anonymous survey. This survey covered heavy drinking behaviors and social network properties. Logistic regression models were conducted to identify social support sources associated with SGM-YAEH’s heavy drinking. Results Over 40 % of SGM-YAEH were involved in heavy drinking in the past 30 days. Receiving support from street-based peers (OR=1.9; 95 % CI=1.1, 3.2) and home-based peers (OR=1.7; 95 % CI=1.0, 2.8) were each positively associated with SGMYAEH heavy drinking risks. Conclusion This study was not able to identify the protective role social supports may play in reducing SGM-YAEH’s heavy drinking. Furthermore, receiving support from network members was correlated with elevated heavy drinking risks among this population. As heavy drinking prevention programs develop interventions: they should use affirming and trauma approaches to promote protective social ties, as research points to its association in reducing alcohol use disparities among SGM-YAEH.

Given reported high rates of transience and service disengagement among youth experiencing homelessness (YEH), new forms of information and communication technologies (ICT) may represent a novel avenue for intervention. Although studies suggest that ICT use is surprisingly high among YEH, less is known about the factors associated with ICT use and the association between ICT use and online health information seeking behavior (OHISB) among YEH. Using Andersen's behavioral model of healthcare utilization as a theoretical framework, this study examined the prevalence of ICT use and its association with OHISB among YEH. Data were drawn from the cross-sectional study collected from YEH living in multi-sites (N = 1,426; Mage = 20.9). Multivariate logistic regression analyses were conducted to examine the factors associated with ICT use (i.e., access to a smartphone, mobile phone, and social media profile ownership) and the association between each ICT use and OHISB. The results showed that differences in ICT use among YEH based on different predisposing factors including YEH's gender, race/ethnicity, employment status, and involvement in the public system. Analysis also revealed that access to mobile and smart phones are significantly associated with OHISB among YEH. Results from this study underscore the capacity of ICT use to efficiently address the health-related requirements of YEH and enhance their health outcomes. The creation of technology-driven interventions, specifically utilizing ICTs, has the potential to assist YEH in accessing diverse information and resources related to their health, thereby fostering improvements in their OHISB.

Young adults experiencing homelessness (YAEH) are at higher risk for intimate partner violence (IPV) victimization than their housed peers. This is often due to their increased vulnerability to abuse and victimization before and during homelessness, which can result in a cycle of violence in which YAEH also perpetrates IPV. Identifying and addressing factors contributing to IPV perpetration at an early stage can reduce the risk of IPV. Yet to date, research examining YAEH’s IPV perpetration is scarce and has largely employed conventional statistical approaches that are limited in modeling this complex phenomenon. To address these gaps, this study used an interpretable machine learning approach to answer the research question: What are the most salient predictors of IPV perpetration among a large sample of YAEH in seven U.S. cities? Participants ( N = 1,426) on average were 21 years old ( SD = 2.09) and were largely cisgender males (59%) and racially/ethnically diverse (81% were from historically excluded racial/ethnic groups; i.e., African American, Latino/a, American Indian, Asian or Pacific Islander, and mixed race/ethnicity). Over one-quarter (26%) reported IPV victimization, and 20% reported IPV perpetration while homeless. Experiencing IPV victimization while homeless was the most important factor in predicting IPV perpetration. An additional 11 predictors (e.g., faced frequent discrimination) were positively associated with IPV perpetration, whereas 8 predictors (e.g., reported higher scores of mindfulness) were negatively associated. These findings underscore the importance of developing and implementing effective interventions with YAEH that can prevent IPV, particularly those that recognize the positive association between victimization and perpetration experiences.

Objective: This study examines the association between public assistance use and depressive symptoms among young adults experiencing homelessness (YAEH). Method: A purposive sample of 1,342 YAEH (ages 18-26) residing in seven U.S. cities (Denver, CO; Houston, TX; Los Angeles and San Jose, CA; New York, NY; Phoenix, AZ; and St. Louis, MO) were asked whether they received any form of public assistance over the past year, of which 45% did. The Patient Health Questionnaire-9 was used to assess depressive symptoms. We used propensity score matching to balance the two groups (i.e., public assistance recipients and nonrecipients) and account for selection bias, then we conducted multivariate ordinary least squares regression analysis to examine the relationship between public assistance and depressive symptoms. Results: YAEH who had used public assistance had a significantly higher depression score (b=1.13, p=.004) than nonrecipients. Conclusions: Findings suggest many possible connections between public assistance use and depression, yet public assistance does not appear to improve mental health for a population facing multiple challenges and barriers. Further research is needed to understand the directional relationship between public assistance and mental health for this population. Social workers should be especially attentive to the mental well-being of young, homeless public assistance recipients.

PurposeThis study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.Design/methodology/approachParticipants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.FindingsFacebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.Originality/valueDigital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.

Purpose: When compared to the general population, people experiencing homelessness have significantly higher rates of TBI (traumatic brain injury). Individuals experiencing homelessness and a TBI require social support because it can serve as a protective factor in reducing the risks of substance use and positively impact housing stability. This study aimed to better understand how social networks influence housing stability among individuals experiencing homelessness and a TBI. Materials and methods: A purposive sampling design was utilized to recruit and survey 115 adults experiencing homelessness. Quantitative questions captured data on demographic information, brain injury-related variables, homelessness-related variables, social network support types and characteristics, and correlates of housing instability including self-report substance use variables. Results: Findings showed that substance use was, indeed, a barrier to stay in or afford housing. Additionally, rates of social support were uniformly low across the sample, showing the unique vulnerabilities associated with homelessness and TBI and homelessness in general. Conclusion: Intervention efforts may consider fostering support networks, as social support has been linked to both housing stability and non-housing outcomes such as reduced substance use, improved health, and community reintegration.

How do people make judgments about characteristics of their immediate their peers? We investigate what cognitive strategies underlie peer judgments, what group-level patterns of judgments these strategies produce, and whether they generate accurate judgments. We develop a general model that allows for comparison of different cognitive strategies including ego projection, probability matching, and three memory-based strategies. We examine it using a unique data set including self-reports and estimates of peer substance use among homeless youth (N=239). We find evidence for the adaptive use of strategies that are most appropriate given the information available from one’s personal experience and social environment. On the group level, the pattern of judgments sometimes resembles false consensus and sometimes false uniqueness, but overall shows a high level of accuracy.

The social and economic empowerment of women using digital technology is critical to global development. This article presents an expanded theory of change to explain how digital technology could augment pathways to women’s empowerment in self-help groups (SHGs), developed through an application of social capital theories to two existing theories of change. Drawing on the analysis of secondary data, it discusses SHGs in India, the determinants of mobile phone use among women in India, particularly patriarchal gender norms, and select theories of change for women’s empowerment in SHGs. The proposed enhanced theory of change has the potential to assist implementers, policymakers, researchers and SHGs themselves to more effectively leverage the empowering potential of digital technology to improve the economic and social opportunities for women in the context of global development strategies.

Young adults experiencing homelessness (YAEH) have high rates of mental health problems but low rates of mental health service use. This study examined identification of mental health problems among YAEH in seven U.S. cities and its relationship to service use. YAEH that screened positive for depression, psychological distress, or Post Traumatic Stress (n = 892) were asked whether they felt they had a mental health problem. One-third identified as having a mental health problem (35%), with 22% endorsing not sure. Multinomial logistic regression models found that older age, cisgender female or gender-expansive (compared to cisgender male), and LGBQ sexual orientation, were positively associated with self-identification and Hispanic race/ethnicity (compared to White) was negatively associated. Self-identification of a mental health problem was positively associated with use of therapy, medications, and reporting unmet needs. Interventions should target understanding mental health, through psychoeducation that reduces stigma, or should reframe conversations around wellness, reducing the need to self-identify.

BACKGROUND:Substance use and other health-risk risk factors, including mental health, trauma, and sexual-risk behaviors, often co-occur among youth experiencing homelessness (YEH). The present study aimed to identify subgroups of YEH based on polysubstance use and the linkages to sociodemographic and health-risk characteristics. METHODS:From June 2016 to July 2017, 1,426 YEH (aged 18-26 years) were recruited from seven cities (Houston, Los Angeles, Denver, Phoenix, New York City, St. Louis, San Jose). Participants provided information via a self-administered electronic survey on substance use, mental health, trauma, sexual risk behaviors, and sociodemographic characteristics. The majority of YEH identified as Black (37.3%), cisgender (92.8%), and heterosexual (69.2%). On average, YEH were 20.9 years (SD = 2.1). This study employed latent class analysis (LCA) to identify subgroups of YEH according to their substance use. Multinomial logistic regression analyses were conducted to identify sociodemographic and health-risk characteristics associated with class-membership. RESULTS:Four latent classes of YEH substance use were identified: (1) high polysubstance use; (2) moderate stimulant and high marijuana, alcohol, and prescription drug use; (3) high marijuana, alcohol, and prescription drug use; (4) low/moderate marijuana and alcohol use. Multinomial logistic regression models indicated that geographic location, gender, race/ethnicity, mental health, trauma history, and sexual risk behaviors were significant correlates of substance use class membership among YEH. CONCLUSIONS:These findings offer important implications for the prevention and treatment of substance use among YEH. Screening protocols should consider co-occurring risk factors such as traumatic experiences, sexual risk behaviors, and mental health history as indicators of polysubstance use.

The Society for Social Work and Research (SSWR) created its Research Capacity and Development Committee in 2017 to build research capacity across the careers of social work scholars. The committee has initiated multiple conferences and webinar sessions that have increasingly focused on antiracist and antioppressive (ARAO) research, including "Mentorship for Antiracist and Inclusive Research" and "Strategies for Supporting Antiracist Pedagogy & Scholarship: Reimagining Institutional Systems & Structures." This commentary integrates themes from these sessions and other discussions among committee members about strategies to advance ARAO research. Although SSWR board members reviewed and approved this submission, it is not an official statement of SSWR or its board of directors.

Limited research has explored whether young adults that experience homelessness and housing instability (YAEH) self-identify as homeless. This study uses the Homeless Youth Risk and Resilience Survey (HYRRS) dataset, a seven-city sample of 1426 YAEH, to examine the relationship between the type of place a young adult stayed the night before their interview and whether they self-identified as homeless. While all participants were classified as homeless as aligned with the McKinny-Vento Homeless Assistance Act of 1987, about one third (30.66%) did not self-identify as homeless. Logistic regression analyses revealed a statistically significant relationship between the type of place stayed and self-identification as homeless; participants who slept outside the night before the interview were most likely to identify as homeless, followed by participants who stayed in shelters/institutions, then participants who couch surfed. Participants who had spent more total time unstably housed were more likely to identify as homeless, as were participants who identified as cisgender women. Findings from this study may be used to inform outreach and engagement practices among service providers, who may consider building partnerships across service sectors so unstably housed young people may access housing support without self-identifying as homeless.

Young adults experiencing homelessness (YAEH) are up to 12 times more likely to contract HIV than their housed peers. One important HIV prevention tool is pre-exposure prophylaxis (PrEP), though few studies examine YAEH PrEP use, particularly among those who identify as lesbian, gay, or bisexual (LGB). This study surveyed 160 YAEH about sexual risk behaviors, knowledge about and interest in using PrEP, and facilitators and barriers to PrEP use. Chi-Squared tests assessed if participants varied on these factors by sexual orientation. LGB YAEH were more likely to have heard of and be interested in using PrEP, as well as report access to free sexual healthcare, counseling about sex life, and concerns about possible medication interactions if they became HIV t as important motivating factors for PrEP use. Straight YAEH were more likely to report concerns about PrEP protectiveness as important. These findings, though preliminary, have important implications for YAEH HIV prevention programs.

Young adults experiencing homelessness (YAEH) with pregnancy history are at higher depression risk. Receiving social support is protective for depression in pregnancy. This study differentiates social support sources associated with depression by pregnancy history among YAEH.Using a subsample of data collected from YAEH in seven US cities that were collected through REALYST, we conducted stratified logistic regression models (by pregnancy history) to identify support sources associated with depression. Logistic regression analysis including the interaction term (i.e., pregnancy history x support sources) using the full sample was then conducted.A higher proportion with pregnancy history reported depression compared to those without. Support from home-based peers was significantly associated with reduced depression risks among YAEH with pregnancy history, but not among youth without. Home-based supports were less frequently indicated by homeless female youth with pregnancy experience.Home-based social support is protective against major depression for YAEH with pregnancy experience. Findings of this study suggest that interventions addressing depression among YAEH should take their pregnancy history and social support sources into consideration. Specifically, for YAEH with pregnancy history, facilitating supportive social ties with home-based peers may be promising in reducing their depression risks.

Homeless youth are a highly vulnerable population and report highly elevated rates of substance use. Prior work on mitigating substance use among homeless youth has primarily relied on survey data to get information about substance use among homeless youth, which can then be used to inform the design of targeted intervention programs. However, such survey data is often onerous to collect, is limited by its reliance on self-reports and retrospective recall, and quickly becomes dated. The advent of social media has provided us with an important data source for understanding the health behaviors of homeless youth. In this paper, we target this specific population and demonstrate how to detect substance use based on texts from social media. We collect 135K Facebook posts and comments together with survey responses from a group of homeless youth and use this data to build novel substance use detection systems with machine learning and natural language processing techniques. Experimental results show that our proposed methods achieve ROC-AUC scores of 0.77 on identifying certain kinds of substance use among homeless youth using Facebook conversations only, and ROC-AUC scores of 0.83 when combined with answers to four survey questions that are not about their demographic characteristics or substance use. Furthermore, we investigate connections between the characteristics of people's Facebook posts and substance use and provide insights about the problem.

We examine the challenges formerly homeless young adults (FHYAs) face after they transition out of homelessness. Considering the adversities FHYAs face, it is unclear how transitioning to stable housing may affect their mental well-being or what types of stressors they may experience once housed. This study investigates the social environment young adults encounter in their transition to stable housing and examines trauma and social coping predictors of mental health symptoms in a sample of FHYAs to generate new knowledge for better intervening to meet their needs. Data were obtained from REALYST, a national research collaborative comprised of interdisciplinary researchers investigating young adults' (ages 18–26) experiences with homelessness. Cross-sectional data for 1426 young adults experiencing homelessness were collected from 2016 to 2017 across seven cities in the United States (i.e., Los Angeles, Phoenix, Denver, Houston, San Jose, St. Louis, and New York City). The analytical sub-sample for this study consisted of 173 FHYAs who were housed in their own apartment (via voucher from Housing and Urban Development or another source) or in transitional living programs during their participation in the study. Ordinary Least Squares regression was used to examine the influence of trauma and social coping strategies on indicators of mental well-being. Findings indicated that higher adversity scores and higher mental health help-seeking intentions were positively associated with higher levels of stress, psychological distress, and depression severity. Higher level of social coping was associated with lower levels of depression severity. Logistic regression results showed that young adults with higher adversity scores had higher odds of reporting clinical levels of post-traumatic symptoms. The study implications suggest that FHYAs who transition to stable housing continue to need support navigating and coping with stressful life events; and interventions that help FHYAs develop strong networks of social supports are needed to promote positive mental well-being.