Police harassment is a form of structural violence with harmful health impacts on young adults experiencing homelessness (YAEH). Yet quantitative research examining how police harassment of YAEH may differ across identities and whether those patterns shift once unstably housed remains limited. This study examines the prevalence and associations of police harassment among YAEH in their lifetime and changes in such once unstably housed. Data come from a cross-sectional sample of 686 YAEH ages 18-25 recruited from urban drop-in centers in Los Angeles, CA, and St. Louis, MO (2023-2025). Police harassment was measured as a dichotomous indicator of lifetime exposure and exposure once unstably housed. Multivariable logistic regression models estimated associations with demographic characteristics, homelessness experiences, and arrest history. Study found 62% of participants reported lifetime police harassment, and 46% reported harassment since experiencing homelessness. In their lifetimes, Black, Latine, and multiracial youth reported higher odds of police harassment compared to White YAEH, and once experiencing homelessness, these racial disparities were no longer statistically significant. Transgender and gender non-conforming YAEH had elevated odds across both models, as well as YAEH with arrest history, those living unsheltered, and those with longer durations of time with unstable housing. Police harassment of YAEH is pervasive and constitutes a severe public health issue, one that is compounded by structural vulnerabilities, including unstable housing, racial/ethnic and gender identities, and carceral system involvement. Documented health consequences of police harassment accumulate alongside the already elevated burdens of experiencing unstable housing itself.

Youth experiencing homelessness (YEH) are at increased risk of HIV. Communication among social network members of YEH about sexual health has the potential to promote safer sex practices. We collected self-reported social network data from 731 YEH (age 14-26) accessing homeless youth services in Los Angeles, California, between 2016 and 2018. Multi-level dyadic network analysis was performed to examine the individual- and network-level characteristics associated with communication about condoms, pre-exposure prophylaxis (PrEP), HIV and HIV testing. YEH were significantly more likely to report talking to a social network member about PrEP if the youth was transgender (OR = 3.65, 95% CI 1.53-8.76) or had a gender-expansive identity (OR = 3.06, 95% CI 1.22-7.68). YEH were more likely to talk to a social network member about PrEP if that network member identified as a sexual minority (LGBQ+) (OR = 8.45, 95% CI 1.13-2.77). YEH were also more likely to talk to a romantic/sexual partner about condom use (OR = 5.86, 95% CI 1.25-2.33) or HIV testing (OR = 1.90, 95% CI 1.08-3.35). Study findings highlight opportunities for improving PrEP uptake among transgender and gender-expansive YEH and for emphasizing partner communications within future HIV prevention interventions for youth experiencing homelessness.

In recent years, there has been a significant increase in the number of people displaced by climate-related damage to the physical and social environment. These migrants are more exposed to climate-related environmental damage than others and more vulnerable to its social and health impacts because they possess fewer resources for mitigation and adaptation. Emerging artificial intelligence (AI) tools and approaches may help improve understanding of climate migration and immobility and support more timely, equitable interventions to reduce avoidable harm before, during, and after displacement. While AI systems have already been applied to climate modeling, disaster forecasting, and public health surveillance, their adaptation to the context of climate-induced displacement remains under-studied and unevenly implemented. Specific AI applications can address the lived realities and systemic vulnerabilities of climate migrants, such as anticipatory relocation, equitable health service provision, and sustainable infrastructure in host regions. However, we must first address certain issues such as the risk of fostering greater inequality through inherent biases in training data; developing public-private-academic collaboratives to collect and integrate high-resolution, localized and open-access datasets tailored to address disparities; prioritizing energy-efficient algorithms and hardware and balancing performance with environmental sustainability; and developing responsible models of AI governance that capture co-design and co-ownership of the design process with climate migration stakeholders including vulnerable and affected communities. We therefore call for empirical research to document the effectiveness of current and proposed initiatives to apply AI in supporting climate migration equity and overcoming methodological and operational limitations and implementation risks. By aligning technological innovation with human-centric values and global justice, AI may contribute to shifting climate mobility policy from crisis response toward resilience-building, if paired with rights-based governance and accountable implementation. While most applications remain pilot-based, context-specific, and unevenly evaluated, this article advances a structured framework to guide future empirical research and governance.

Young adults experiencing homelessness (YAEH) are significantly more likely to engage in HIV risk-related sexual behavior relative to their stably housed peers, and their social support networks can influence their engagement in these behaviors. However, few studies have investigated HIV risk behaviors among YAEH who are “couch-surfing,” a highly prevalent network-based survival strategy that involves cycling through temporary, informal housing arrangements. The current study utilizes survey data collected from 461 YAEH accessing drop-in center services in Los Angeles, California, between September 2016 and October 2018. Egocentric network analysis was used to examine associations among couch-surfing, sources of social support, and HIV risk and prevention behaviors. The potential moderating effect of social support on the relationship between couch-surfing and specific sexual risk behaviors was also tested. Compared to street- and shelter-based youth, couch-surfing YAEH reported the highest rates of recent transactional sex (18.0

With the ability to approximate human decision-making and to parse through large and complex data, artificial intelligence (AI) technologies such as machine learning algorithms stand to play important roles in diagnosing, treating, and preventing society's most pressing social, health, and academic challenges. However, some AI applications for these ends have resulted in biased and discriminatory outcomes, demonstrating poor accountability to the communities these technologies impact and serve. The foundational premise of this paper is that AI-augmented prevention science needs community engagement to mitigate these harms. Drawing on a decade of work conducted at the University of Southern California's Center for AI in Society, we present a model of community-engaged AI-augmented prevention science. What sets our formalization apart from previous frameworks for community-engaged AI research is its prevention science orientation. We highlight potential roles for community and AI at each stage of the prevention science life cycle, from problem conceptualization to intervention implementation, and delineate when community input can compel moments of critical retreat in that life cycle to remain accountable to community needs and values. We then illustrate how we integrated AI and community-engaged methods in our work to correct racial biases in a predictive risk algorithm used to prioritize vulnerable people experiencing homelessness for housing interventions. Our case study highlights two moments of critical retreat-informed by the values, experiences, and contextual knowledge of our community partners-that led to more equitable and inclusive outcomes. We conclude with recommendations for how to advance community-engaged approaches in AI prevention science research.

Artificial intelligence researchers have proposed various data-driven algorithms to improve the processes that match individuals experiencing homelessness to scarce housing resources. It remains unclear whether and how these algorithms are received or adopted by practitioners and what their corresponding consequences are. Through semi-structured interviews with 13 policymakers in homeless services in Los Angeles, we investigate whether such change-makers are open to the idea of integrating AI into the housing resource matching process, identifying where they see potential gains and drawbacks from such a system in issues of efficiency, fairness, and transparency. Our qualitative analysis indicates that, even when aware of various complicating factors, policymakers welcome the idea of an AI matching tool if thoughtfully designed and used in tandem with human decision-makers. Though there is no consensus as to the exact design of such an AI system, insights from policymakers raise open questions and design considerations that can be enlightening for future researchers and practitioners who aim to build responsible algorithmic systems to support decision-making in low-resource scenarios.

Studying peer relationships is crucial in solving complex challenges underserved communities face and designing interventions. The effectiveness of such peer-based interventions relies on accurate network data regarding individual attributes and social influences. However, these datasets are often collected through self-reported surveys, introducing ambiguities in network construction. These ambiguities make it challenging to fully utilize the network data to understand the issues and to design the best interventions. We propose and solve two variations of link ambiguities in such network data – (i) which among the two candidate links exists, and (ii) if a candidate link exists. We design a Graph Attention Network (GAT) that accounts for personal attributes and network relationships on real-world data with real and simulated ambiguities. We also demonstrate that by resolving these ambiguities, we improve network accuracy, and in turn, improve suicide risk prediction. We also uncover patterns using GNNExplainer to provide additional insights into vital features and relationships. This research demonstrates the potential of Graph Neural Networks (GNN) to advance real-world network data analysis facilitating more effective peer interventions across various fields.

Objective: To examine prospective, bidirectional associations between homelessness and substance use frequency among young adults receiving substance use treatment in the United States. We also investigated potential differences across demographic subgroups. Methods: Young adults (N = 3717, Mage = 20.1, 28% female, 7.3% sexual/gender minority, and 37% non-Hispanic White) receiving substance use treatment in the U.S. completed assessments at intake, 3 months, 6 months, and 12 months post-intake. Latent growth curve models with structured residuals (LGC-SR) were used to examine cross-lagged associations between homeless days and frequency of substance use and associated problems. Models were stratified by sex, race/ethnicity, and sexual and/or gender minority status. Results: Overall, days spent homeless (mu(slope )= -0.19, p = 0.046) and substance use frequency (mu(slope1 )= -6.19, p < 0.001) significantly decreased during treatment, with no significant cross-lagged associations between homeless days and substance use frequency. However, results differed by race and ethnicity. For non-Hispanic White young adults, greater substance use at treatment entry was associated with steeper declines in homeless days between-persons (phi(standardized) = -0.14, p = 0.04). For African Americans, homeless days at treatment entry were associated with greater increases in substance use between-persons (phi(standardized) = 0.29, p = 0.04). No significant differences were found by sex or sexual/gender minority status. Conclusions: Despite overall declines in homelessness and substance use during treatment, these outcomes may unfold differently for non-Hispanic White and African American young adults. More support may be needed for African American young adults reporting homelessness at treatment entry.

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.

Transgender and gender non-conforming (TGNC) individuals experience greater social adversity and negative social determinants of health compared to cisgender people. This study explored housing security and service use within a probability-based, nationally representative sample. Respondents were adults (264 TGNC; 23,096 cisgender) from the U.S. Federal Reserve Survey of Household Economics and Decision-making. Chi-square and multiple logistic regression analyses compared differences between TGNC and cisgender respondents for housing security indicators and government housing support use. Multivariable analyses were weighted for complex sampling design. Despite a large proportion of TGNC respondents reporting inability to pay their bills (27.9%) and not being homeowners (62.0%), utilization rates for government housing assistance were low (10.4%). Adjusting for covariates, TGNC respondents had significantly greater odds of reporting governmental housing assistance (aOR = 2.64, 95%CI = 1.52-4.60) than cisgender respondents. There was no difference in ability to pay bills or homeownership. Findings indicate that government housing services are reaching TGNC adults relative to cisgender results, but utilization rates remain low despite indicators of high need. Factors influencing utilization and efficacy of services should be assessed in future research to support policy implementation.

In the United States, an estimated 4.2 million young people experience homelessness during critical stages in their development—adolescence and emerging adulthood. While research on youth homelessness often emphasizes risk and vulnerability, the field must situate these issues within the developmental trajectories of adolescence and emerging adulthood to effectively prevent and end youth homelessness. This review uses the Risk Amplification and Abatement Model (RAAM) as a conceptual framework for contextualizing the landscape of youth homelessness research in the United States since 2010. An extension of ecological models of risk-taking, RAAM emphasizes both risk and resilience, positing that negative as well as positive socialization processes across interactions with family, peers, social services, and formal institutions affect key housing, health, and behavioral outcomes for youth experiencing homelessness. This review applies RAAM to our understanding of the causes and consequences of youth homelessness, recent interventions, and recommendations for future directions.

Warning: Contents of this paper may be upsetting. Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet challenging to understand at scale. We study one such social issue: homelessness in the U.S., by leveraging the remarkable capabilities of large language models to assist social work experts in analyzing millions of posts from Twitter. We introduce a framing typology: Online Attitudes Towards Homelessness (OATH) Frames: nine hierarchical frames capturing critiques, responses and perceptions. We release annotations with varying degrees of assistance from language models, with immense benefits in scaling: 6.5x speedup in annotation time while only incurring a 3 point F1 reduction in performance with respect to the domain experts. Our experiments demonstrate the value of modeling OATH-Frames over existing sentiment and toxicity classifiers. Our large-scale analysis with predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in attitudes across states, time periods and vulnerable populations, enabling new insights on the issue. Our work provides a general framework to understand nuanced public attitudes at scale, on issues beyond homelessness.

Next article No AccessFinancial Well-Being and Social Service Use among a Nationally Representative Sample of Sexual and Gender Minority AdultsMaiya Hotchkiss, Eric Rice, and John R BlosnichMaiya Hotchkiss, Eric Rice, and John R BlosnichPDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by Journal of the Society for Social Work and Research Just Accepted Published for the Society for Social Work and Research Article DOIhttps://doi.org/10.1086/730543 PermissionsRequest permissions HistoryAccepted March 11, 2024 © 2024 Society for Social Work and Research. All Rights reserved.PDF download Crossref reports no articles citing this article.

Introduction: The health needs of persons experiencing homelessness (PEH) are complex and challenging to address. We currently lack a comprehensive understanding of the health of PEH across the United States given that this information is not routinely collected, in part due to lack of an objective measure. We combined robust community advisory board member input and findings from an extensive literature review to identify existing assessment tools, definitions of health and care needs, and items that assess health and care needs among PEH. Current assessment tools focus on prioritizing PEH for housing and shelter, support clinical planning, measure health vulnerability, or are topically relevant, but not designed for use with PEH. Moreover, available tools have undergone minimal psychometric testing with PEH. To overcome this gap, we propose the development of a reliable and validated tool that measures the health and care needs of PEH. Having a standardized tool will enable the comparison of needs data among PEH across studies, as well as over time. Thorough measurement of the health and care needs of PEH will allow for the evaluation of interventions that aim to improve health outcomes and reduce health disparities in this population.