Despite high rates of juvenile justice system involvement among youth emancipating from foster care, we continue to know very little about how independent living services (ILS) receipt may be differentially associated with young adult outcomes between dual-system and non-dual-system youth. Using data from the National Youth in Transition Database (NYTD), this study examines how the association between criminal justice system involvement in young adulthood and ILS receipt depends on dual-system status. Survey non-response weights are calculated using child welfare system history and demographic measures. Models are fit separately to predict incarceration between 17-19 and 19-21. Results show that receipt of ILS before 17 is significantly associated with decreased risk of incarceration between 17-19 for all youth, regardless of dual-system status. The results suggest that the relationship between ILS and incarceration does not depend on dual-system status at the national level, but does when accounting for state variation.

Child maltreatment (CM) is a significant and global public health concern with a complex array of causes and consequences. Identifying the most impactful modifiable factors at all levels of the ecosystem is a significant challenge for policymakers, practitioners, students, and researchers trying to grasp the multidisciplinary literature that accompanies such complex and dynamic problems. This article describes the process of creating a public facing dynamic literature map using a novel application of Group Model Building (GMB), as systems science method, with academic and field experts to develop a qualitative map of hypothesized paths to and following CM and then link this to empirical findings. The goal of the "map" was to address the problem of creating a publicly accessible summary of knowledge across disciplines that helps identify constructs and paths that may have received greater or lesser research attention so that it reflects a high level, and updateable, summary of what is known and gaps for further research. This multi-year process creates an accessible tool through use of electronic interactive visual mapping software in the hopes of better informing practice, policy, and research innovation. The present article provides a summary of the process and introduction to an Alpha version of this electronic map.

Purpose: Prior literature using data from the National Youth in Transition Database (NYTD) has found that rates of independent living services (ILS) receipt are not uniform across youth aging out of foster care. At least two gaps in this literature remain: Research has been limited to cross sectional analyses and the degree to which specific placement types associate with ILS receipt is unclear. Additionally, given high rates of delinquency among transition aged youth, research on youth receiving ILS needs to prioritize understanding how child welfare system trajectories predict dual-system onset. Using a multistate model and administrative data in the United States, this study asks the following questions: (1) How does ILS receipt vary as a function of placement type? and (2) Among youth who receive ILS, how does placement type associate with dual system involvement onset? Methods: The NYTD Services file and Adoption and Foster Care Analysis and Reporting System (AFCARS) 6-month files were used to construct longitudinal data identifying youths' ILS receipt status, delinquency status, and current placement type for consecutive 6-month fiscal year (FY) periods. Additional time-fixed measures on select youth demographics were included. Youth were categorized into one of five mutually exclusive states in each 6-month period: (1) not receiving ILS and not delinquent, (2) receiving ILS and not delinquent, (3) not receiving ILS and delinquent, (4) receiving ILS and delinquent, and (5) emancipated. A Markov multistate model was fit to examine how placement type and demographics predicted transitions between states. Results: Group home and institution placements predicted decreased probability of starting ILS (HR = 0.79, p < 0.05), and relative foster care placements predicted decreased probability of starting ILS (HR = 0.90, p < 0.05) and increased probability of stopping ILS (HR = 1.28, p < 0.05). Trial home visits stand out as being predictive of dual-system onset for youth receiving ILS (HR = 4.05, p < 0.05). Hispanic youth experienced greater ILS instability, being more likely to uptake services (HR = 1.24, p < 0.05) but also more likely to stop receiving services (HR = 1.28, p < 0.05). Conclusions: Findings indicate that agencies should prioritize ILS outreach efforts around issues of recruitment and retention when youth change placement types. Agencies should also consider providing additional supports to youth transferring to trial home visits to prevent delinquency.

BACKGROUND:The Report and Placement Integrated Data System (RAPIDS) integrates two U.S. national data systems-NCANDS' child maltreatment report (CMR) records and AFCARS' foster care (FC) records-into a single longitudinal dataset spanning 2006-2021. This integration enables comprehensive child maltreatment analysis by linking the annual files from these previously separate systems. OBJECTIVE:To explore benefits of RAPIDS data in understanding CMR outcomes. PARTICIPANTS AND SETTING:Children aged 0-10 years with CMRs in 2018 (N = 2,371,119). METHODS:Using logistic regression, we modeled five outcomes: two current outcomes from 2018 index reports (substantiation and foster care entry) and three future outcomes within two years (re-report, substantiated re-report, and foster are entry). For each outcome, we compared models using only index report data without RAPIDS variables against models incorporating RAPIDS-enabled variables that capture longitudinal patterns across reports, placements, and siblings. RESULTS:RAPIDS data improved model performance across all outcomes, with greater gains for future outcomes. Overall model fit (Tjur's R2) increased for substantiation (11.75 % → 12.53 %), FC entry (4.17 % → 6.38 %), rereport (0.94 % → 4.99 %), substantiated rereport (1.04 % → 3.31 %), and future FC entry (0.85 % → 2.51 %). Predictive performance also improved: at 80 % sensitivity, specificity increased for substantiation (54 % → 56 %), FC entry (52 % → 58 %), rereport (27 % → 36 %), substantiated rereport (33 % → 42 %), and future FC entry (37 % → 49 %). Additionally, RAPIDS data enabled analysis of a wider array of predictors and their associations with outcomes, fully utilizing national longitudinal CMR and FC records. CONCLUSIONS:RAPIDS data enhance explanatory power and predictive accuracy, enabling nationwide, longitudinal analysis of CMR and FC records and offering valuable insights into risk and protective factors.

Many children in foster care experience trauma that is rooted in unstable family relationships. Other members of the foster care system like foster parents and social workers face secondary trauma. Drawing on 10 years of Reddit data, we used a mixed methods approach to analyze how different members of the foster care system find support and similar experiences at the intersection of two Reddit communities - foster care, and abuse. We found that users who cross the boundary between the two communities focus on trauma experiences specific to different roles in foster care. While representing a small number of users, cross-posters contribute heavily to both communities, and, compared to other community members, receive higher scores and more replies. We explore the roles boundary crossing users have both in the online community and in the context of foster care. Finally, we present design, practice, and policy recommendations that would support survivors of trauma find communities more suited to their personal experiences.

To promote the early identification and support of children at risk for developmental issues and other adversities, seven of Orange County, California’s largest birthing hospitals have adopted a “hospital screening” process comprised of an initial clinical data scan, followed by the completion of a more robust bedside assessment. This approach to hospital screening and referral, termed the “Bridges Program,” is used to connect parents of high-risk newborns to home visiting services aimed at providing support and resources. Orange County also has four birthing hospitals that do not participate in the Bridges Program, which allowed for the comparison of births occurring between 2011 and 2012 at Bridges hospitals (n = 53,302) to births at non-Bridges hospitals (n = 25,146) on various child protection outcomes by age five, accounting for program engagement and demographic differences. Bridges assessments successfully identified births at higher risk for child protection service involvement, and high-risk Bridges births not forwarded for agency outreach were significantly more likely to be reported and substantiated for maltreatment compared to low risk Bridges births (RR = 1.18, CI = [1.08, 1.30]; RR = 1.30, CI = [1.10, 1.54]). These results highlight a subset of births that would benefit from alternative strategies for engaging with home visiting services.

ObjectiveTo evaluate the fairness of a machine learning (ML) model designed to assess the need for home visiting services, focusing on its performance across family characteristics.BackgroundML models are increasingly used in family-centered services; however, their fairness remains underexplored, particularly concerning family sociodemographic factors and service contexts.MethodsThis study assessed the fairness of an ML model developed for home visiting services examining false negative rates (FNRs) across subgroups, particularly focusing on the intersection of maternal ethnicity and nativity.ResultsThe ML model reduced FNRs from 52.9% to 22.1%, with the most notable improvements for children of Black mothers and with family characteristics associated with high risk. However, the model was less effective for children of Asian and foreign-born Hispanic mothers.ConclusionAlthough the ML model substantially reduced FNRs across various family subgroups, disparities were observed.ImplicationsUnderstanding fairness in ML models requires a thoughtful approach, considering service context and impact on the families from diverse backgrounds. Continued research and collaboration are necessary for fair and inclusive use of ML models for family-centered services.

The majority of children referred to child protective services (CPS) system are not suspected of physical or sexual abuse but face other risks, often captured under neglect. This population-based study analyzed data for 145,745 children referred to the Pennsylvania's child welfare system in 2021. Parental substance use (28%), child behavioral concerns (24%), and unmet material needs (23%) were the most common allegations. Half of referrals included multiple concerns, but only 34% were valadated, indicating service intervention. Child behavioral health was the most frequent standalone concern (14%). Increasing access to mental health and substance use treatment may reduce CPS referrals.

Assessing and responding to risks to children's safety is a primary concern of the child protection system (CPS), and decision-support tools have been developed to assist child welfare workers (CWW). Yet, a limited understanding of CWWs' decision-making experiences impedes our efforts to effectively support them. This qualitative study examines the unique characteristics of decision-making in CPS through focus groups involving CWWs from an agency in California. Five themes emerged: CWWs' responsibilities, decision-making characteristics, domain-specific complexities, and CWWs' perspectives on fairness in decision-making. The findings highlight the need to incorporate CWWs' experiences and insights in developing future decision-support tools.

Rationale: Children from multicultural families in Korea are vulnerable to bullying victimization. Despite growing evidence on the mental health consequences of bullying victimization for victims, little is known about the spillover effects on their immigrant mothers. Objective: This study examined the association between children's bullying victimization and their immigrant mothers' suicidal ideation. The potential moderating role of family socioeconomic status was also investigated. Methods: Using nine waves of the Multicultural Adolescents Panel Study spanning from 2011 to 2019 (N =1466), this study estimated individual fixed effects models to control for unobserved individual-level heterogeneity. Interaction models were used to investigate potential heterogeneity by family socioeconomic status, including maternal education, household income, and maternal occupational status. Results: Fixed effects estimates revealed that children's bullying victimization is associated with an increased likelihood of suicidal ideation among marriage migrant mothers (b = 0.012, p < 0.05), even after controlling for unobserved time-invariant confounders as well as a set of time-varying covariates. Family socioeconomic status moderated this association. The association between children's bullying victimization and immigrant mothers' suicidal ideation was stronger for those with low levels of education and household income. No such moderating effects were observed for maternal occupation. Conclusions: The findings of this study suggest that efforts to address the mental health consequences of bullying victimization among multicultural family children should extend beyond the victim to their immigrant mothers. When developing interventions to reduce suicidal ideation among immigrant mothers whose children have been victimized, policymakers may wish to consider the moderating role of family socioeconomic status.

Objective: This scoping review systematically examined the applied family science literature involving families raising young children to understand how relevant studies have applied artificial intelligence (AI)-facilitated technologies. Background: Family research is exploring the application of AI. However, there is a critical need for a review study that systematically examines the varied use of AI in applied family science to inform family practitioners and policymakers. Method: Comprehensive literature searches were conducted in nine databases. Of the 10,022 studies identified, 21 met inclusion criteria: peer-reviewed journal article; published between 2014-2024; written in English; involved the use of AI in collecting data, analyzing data, or providing family-centered services; included families raising young children 0-5 years; and was quantitative in analysis. Results: Most studies focused on maternal and child health outcomes in low- and middle-income countries. All studies identified were in the AI use domain of data analysis, with 76% of the studies having a focus on identifying the most important predictors. Random forest performed as the best machine learning model. Only one study directly mentioned the ethical use of AI. Conclusion: Overall, the applied family science evidence base that employs AI is limited in size and scope, with most studies using AI for data analysis purposes with limited ethical considerations. Implications: AI models in applied family science can inform family services and policies aimed at promoting family and child health. However, thoughtful consideration of AI ethics and fairness is needed to prevent the negative social impacts of AI on marginalized groups of families and their young children.

The practice of family separation as a mechanism of oppression has a deep-rooted history in the U.S., manifesting in diverse contexts, including punitive migration policies. This systematic review aimed to provide a rigorous and updated synthesis of the research on family separation as a result of migration policies and its impacts on immigrants’ mental health while making a distinction between forced family separation, family separation by constrained choices, and living with the fear of family separation. We systematically searched four bibliographic databases using keywords related to family separation, migration, transnational families, and mental health for peer-reviewed studies published in English on or before January 1st, 2022. Results of the review indicate that family separation or fear of it may result in depression, anxiety, behavioral and emotional issues, sleep disturbances, and stress or distress in affected children. Similarly, impacted parents or caregivers might experience stress or distress, depression, anxiety, and sleep disturbances. Findings call for migration policy changes prioritizing family unity and comprehensive mental health interventions to respond to the pervasive consequences of family separation or fear thereof among immigrants in the U.S.

Child maltreatment can affect multiple children in a family, yet its occurrence and chronicity has been often assessed by focusing on a single child. Although this approach provides valuable insights, considering the experiences of all children in a family may provide a more complete understanding of maltreatment dynamics. Using linked birth and child protection system (CPS) records from California, we analyzed 20 years of data on 194,514 first-time mothers to document the prevalence, timing, and chronicity of maternal CPS reporting across multiple children. Mothers were categorized by the number of live childbirths: one (25.7%), two (36.2%), three (20.9%), and four or more (17.2%). Overall, 33.0% of mothers were reported to CPS, increasing from 18.5% for mothers with one child to 63.1% for those with four or more children. For mothers with two or more children, more than 70% experienced an initial CPS report only after the second child’s birth. Our findings have implications for understanding the dynamics of maternal reports to CPS, emphasizing the need for lasting and family-focused interventions.

Background: Early identification of children and families who may benefit from support is crucial for implementing strategies that can prevent the onset of child maltreatment. Predictive risk modeling (PRM) may offer valuable and efficient enhancements to existing risk assessment techniques. Objective: To evaluate the PRM's effectiveness against the existing assessment tool in identifying children and families needing home visiting services. Participants and setting: Children born in hospitals affiliated with the Bridges Maternal Child Health Network in Orange County, California, from 2011 to 2016 (N = 132,216). Methods: We developed a PRM tool by integrating a machine learning algorithm with a linked dataset of birth records and child protection system (CPS) records. To align with the existing assessment tool (baseline model), we limited the predicting features to the information used by the existing tool. The need for home visiting services was measured by substantiated maltreatment allegation reported during the first three years of the child's life. Results: Of the children born in Bridges Network hospitals between 2011 and 2016, 2.7 % experienced substantiated maltreatment allegations by the age of three. Within the top 30 % of children with high-risk scores, the PRM tool outperformed the baseline model, accurately identifying 75.3 %-84.1 % of all children who would experience maltreatment substantiation, surpassing the baseline model's performance of 46.2 %. Conclusions: Our study underscores the potential of PRM in enhancing the risk assessment tool used by a prevention program in a child welfare center in California. The findings provide valuable insights to practitioners interested in utilizing data for PRM development, highlighting the potential of machine learning algorithms to generate accurate predictions and inform targeted preventive services.

Child maltreatment recidivism is typically measured and studied at the individual level. Conditions that give rise to child abuse and neglect, however, typically affect multiple children in a given family. In the current study, we estimated maltreatment recidivism at the maternal level and examined its risk as a function of maternal sociodemographic characteristics that may change over time. Using linked administrative records, we identified a subset of first-time mothers in California whose first child was reported to the child protection system (CPS) between birth and age 5 and who then gave birth to another child ( n = 14,715). Following the firstborn child's CPS reporting, nearly half of these mothers (43.3%) were re-reported concerning the non-firstborn children during the first 5 years of the child's life. Risk factors consistently documented across births were associated with a heightened risk of maternal CPS recidivism. Our study advances an understanding of the full extent of maltreatment recidivism by broadening the focus from individual children.

Abstract The negative health impacts of childhood adversity have been well documented. However, little is known about the pathways linking childhood adversity with cognitive functioning later in life. We addressed this issue by examining the extent to which lifetime sleep problems mediated the effects of childhood adversity on late-life cognitive functioning. We used the 2002-2018 waves of the Health and Retirement Study in which participants were 20,607 adults (58.1% female), aged ≥50 years (Mage=68.9±9.5 years). Cognitive functioning was based on: 1) a 10-word immediate and delayed recall test of memory; 2) a serial 7s subtraction test of working memory; and 3) counting backwards to assess attention and processing speed. Sleep problems (e.g., difficulty initiating sleep) were assessed with the Jenkins Sleep Scale, and seven items of childhood adversity (e.g., physical abuse) were included from the list of lifetime potentially traumatic events. Path analysis revealed that there was a small but significant indirect relationship between childhood adversity and poor cognitive functioning through sleep problems (coefficient = -.014; p=.001). Indeed, about 3% of the effect of childhood adversity on cognitive functioning is mediated by sleep problems (i.e., 0.014 [indirect effect] / 0.476 [total effect]). Our findings can help policymakers and public health practitioners better understand the risk and protective factors of cognitive functioning later in life in relation to sleep problems among those individuals with childhood adverse events. Further research is warranted to determine if childhood adversity contributes to cognitive decline in those with trauma and PTSD compared to those with trauma alone.

The Children's Data Network (CDN) is a data and research collaborative focused on the linkage and analysis of administrative records. In partnership with public agencies, philanthropic funders, affiliated researchers, and community stakeholders, we seek to generate knowledge and advance evidence-rich policies that improve the health, safety, and well-being of the children of California. Given our experience negotiating access to and working with existing administrative data (and importantly, data stewards), the CDN has demonstrated its ability to perform cost-effective and rigorous record linkage, answer time-sensitive policy- and program-related questions, and build the public sector's capacity to do the same. Owing to steadfast and generous infrastructure and project support, close collaboration with public partners, and strategic analyses and engagements, the CDN has promoted a person-level and longitudinal understanding of children and families in California and in so doing, informed policy and program development nationwide. We sincerely hope that our experience—and lessons learned—can advance and inform work in other fields and jurisdictions.

Objective To determine the population prevalence of diagnosed mental health disorders among Medicaid-insured children <18 years old in California based on levels of current and past child protection system (CPS) involvement. Study design In this retrospective, population-based study, we examined the full population of children enrolled in California's Medicaid program for at least 1 month between 2014 and 2015 and who had at least 1 claim during that period (n = 3 352 886). Records for Medicaid-insured children were probabilistically linked to statewide CPS records of maltreatment and foster care placements since 1998. A primary or secondary mental health diagnosis was classified using International Classification of Diseases codes. Results Overall, 14% (n = 470513) of all children insured through Medicaid in 2014-2015 had a documented mental health diagnosis. Among children with a diagnosis, the percentage with CPS involvement (ie, any report for maltreatment) was nearly twice that of the Medicaid population overall (50.4% vs 26.9%). This finding held across all diagnostic groups but with notable variations in magnitude. A graded relationship emerged between the level of CPS involvement and the likelihood of a mental health diagnosis. Diagnoses among children reported for maltreatment were common, regardless of placement in foster care. Conclusions Findings document high rates of both mental health diagnoses and past child protection involvement in a population of Medicaid-insured children. Most children reported for maltreatment will never be placed in foster care, underscoring the importance of ensuring that the children who remain at home receive the proper array and coordination of services.

The Children's Data Network (CDN) is a data and research collaborative focused on the linkage and analysis of administrative records. In partnership with public agencies, philanthropic funders, affiliated researchers, and community stakeholders, we seek to generate knowledge and advance evidence-rich policies that improve the health, safety, and well-being of the children of California. Given our experience negotiating access to and working with existing administrative data (and importantly, data stewards), the CDN has demonstrated its ability to perform cost-effective and rigorous record linkage, answer time-sensitive policy- and program-related questions, and build the public sector's capacity to do the same. Owing to steadfast and generous infrastructure and project support, close collaboration with public partners, and strategic analyses and engagements, the CDN has promoted a person-level and longitudinal understanding of children and families in California and in so doing, informed policy and program development nationwide. We sincerely hope that our experience-and lessons learned-can advance and inform work in other fields and jurisdictions.