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Item CANS and ANSA Outcome Reports Reference Guide, version 3(2024) Walton, Betty; Moynihan, Stephanie; Hong, Stephanie; Kwon, HyejeanAlthough behavioral health disorders are common, the quality of care has not kept pace with the quality of physical health care. Measuring behavioral health care quality has slowly evolved. Suggested quality of care initiatives include a routine process (fidelity) and outcome feedback, which has been linked to improved symptoms, quality of life, and lower readmission rates. Regularly discussing and measuring personal change is recommended. To support data-informed decisions based on personal change and to improve service quality, outcome management reports based on the Child and Adolescent Needs and Strengths (CANS) and Adult Needs and Strengths Assessment (ANSA) data were developed in DARMHA, the Indiana Division of Mental Health and Addiction's data collection and reporting platform. This reference guide describes each individual or aggregate report and provides tips to access and utilize the information.Item The Intersectionality of Factors Predictng Co-occurring Disorders: A Decision Tree Model(2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, StephanieIndividuals with co-occurring psychiatric and substance use disorders (COD) face challenges accessing care, accurate diagnoses, and effective treatment. To better understand factors other than substance use, which differentiates COD from psychiatric disorders PD, a study examined the combined effects of age, gender identity, race/ethnicity, pandemic, behavioral health needs, useful strengths, and COD. Involvement in recovery, active participation in treatment and managing one’s health, was the strongest predictor of having COD. This research brief highlights finding and key takeaways with implication for creating accessible, effective services, building life functioning skills, identifying risky behavior, and person-centered recovery planning.Item Improving Treatment Completion for Young Adults with Substance Use Disorder: Machine Learning-Based Prediction Algorithms(2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, StephanieSubstance Use Disorder treatment completion has been associated with positive outcomes, such as reduced relapse rates and longer periods of abstinence. A study identified factors influencing SUD treatment completion among young adults (aged 18–25) receiving publicly funded outpatient services. This research brief describes how a machine learning decision tree model explored interactions between functional behavioral health needs and strengths, criminal justice system involvement, and completing treatment. A machine learning approach made it possible to identify complex relationships among many factors, improving our understanding on where to focus treatment.Item Exploring Disparities in Behavioral Health Service Use in the Early Stages of the Covid-19 Pandemic(2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, StephanieThis research brief highlights the findings and takeaways from a published study comparing behavioral health service use by adults during the early COVID-19 pandemic and the previous year. From 2019 to 2020, admissions increased by 46%. Although individuals with co-occurring mental health and substance use disorders experienced the most complex challenges, the greatest increase in accessing treatment was by people with mental health concerns. More women accessed services than men. Service use increased for Multiracial and Hispanic adults, decreased for African American and White people, and remained stable for American Indians. Different service access patterns and complexity may have been related to pandemic and existing factors.Item Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach(2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, StephanieAs experiencing pandemic related hardships (social isolation, financial distress, and health anxiety) and racial discrimination worsened Asian American’s mental health, a study examined unique behavioral health characteristics of Asian Americans compared to White and Black Americans in behavioral health treatment. Assessment data was analyzed using descriptive and chi-squared automatic interaction detection (CHAID), a machine learning approach, to detect additional differences among groups. Asian Americans had distinct patterns of behavioral health needs compared to White and African Americans. Key takeaways inform culturally responsive practice.Item Longitudinal Patterns of Strengths among Youth with Psychiatric Disorders: A Latent Profile Transition Analysis(2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, StephanieHuman service agencies have historically prioritized interventions mitigating risks rather than leveraging youth and family strengths. For youth with psychiatric disorders, better understanding the variability of strengths is crucial. Strength-based interventions include many dimensions: family strengths, interpersonal relationships, optimism, spirituality, talents and interests, educational setting, involvement in care, natural supports, community engagement, and resilience. A study examined how strengths were related to recovery. This research brief summarizes the study's findings and implications for child behavioral health services.Item The Intersectionality of Factors Predicting Co-occurring Disorders: A Decision Tree Model(Springer, 2024-07-22) Hong, Saahoon; Kim, Hea-Won; Walton, Betty; Kaboi, MaryanneIndividuals with co-occurring psychiatric and substance use disorders (COD) face challenges, including accessing treatment, accurate diagnoses, and effective treatment for both disorders. This study aimed to develop a COD prediction model by examining the intersectionality of COD with race/ethnicity, age, gender identity, pandemic year, and behavioral health needs and strengths. Individuals aged 18 or older who participated in publicly funded behavioral health services (N=22,629) were selected. Participants completed at least two Adult Needs and Strengths Assessments during 2019 and 2020, respectively. A chi-squared automatic interaction detection (CHAID) decision-tree analysis was conducted to identify patterns that increased the likelihood of having COD. Among the decision tree analysis predictors, Involvement in Recovery emerged as the most critical factor influencing COD, with a predictor importance value (PIV) of 0.46. Other factors like Legal Involvement (PIV=0.12), Decision-Making (PIV=0.12), Parental/Caregiver Role (PIV=0.11), Other Self-Harm (PIV=0.10), and Criminal Behavior (PIV=0.09) had progressively lower PIVs. Age, gender, race/ethnicity, and pandemic year did not show statistically significant associations with COD. The CHAID decision tree analysis provided insights into the dynamics of COD. It revealed that legal involvement played a crucial role in treatment engagement. Individuals with legal challenges were less likely to be involved in treatment. Individuals with COD displayed more complex behavioral health needs that significantly impaired their functioning compared to individuals with psychiatric disorders to inform the development of targeted interventions.Item Mindfulness Activities and Techniques: For Clinicans, Adults, and Kids(2024) Ray-Bennett, Kristina"Mindfulness Activities and Techniques: For Clinicians, Adults, and Kids" is a comprehensive guide authored by Kristina Ray-Bennett. It offers a rich collection of evidence-based mindfulness practices. With activities designed for all ages, this book empowers individuals and families to cultivate self-discovery, healing, and resilience. Rooted in neuroscience and enriched by personal experiences, this resource provides practical tools for managing anxiety, depression, PTSD, and OCD, fostering well-being in both personal and professional settings.Item Online Social Deception and Its Countermeasures: A Survey(IEEE, 2021) Guo, Zhen; Cho, Jin-Hee; Chen, Ing-Ray; Sengupta, Srijan; Hong, Michin; Mitra, Tanushree; School of Social WorkWe are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building trustworthy SNSs. In this paper, we conduct an extensive survey, covering 1) the multidisciplinary concept of social deception; 2) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; 3) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; 4) datasets/metrics used for validation and verification; and 5) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons learned from the existing literature. We conclude our survey with in-depth discussions on the limitations of the state-of-the-art and suggest future research directions in OSD research.Item The Impact of Life Domains on Delinquent Behaviors in Five Caribbean Countries: A Partial Test of Agnew’s General Theory of Crime and Delinquency(Springer, 2022-02) Roh, Myunghoon; Cho, Sujung; Nolasco Braaten, Claire Angelique; Kim , Jangmin; Kim, Jeongsuk; Gentle-Genitty, Carolyn; School of Social WorkThe current study tests the applicability of Agnew’s (2005) general theory of crime and delinquency to a sample of Caribbean Community (CARICOM) youths and explains the hypothesized direct and indirect/mediated effects of family attachment and peer delinquency on delinquent behaviors. Data for this study were obtained from a 2014 cross-sectional survey of 512 adolescents from the five members of the CARICOM. This study utilizes mediation analysis. Results reveal that adolescents with abuse experience from family members and unsafe school environments are more likely to engage in delinquent behavior. Furthermore, peer delinquency is significantly related to delinquent behavior and mediates the link between child abuse, family history of violence, unsafe school environment, and subsequent delinquent behavior. Finally, child abuse generated a lower level of family attachment, and then a higher level of family attachment led to a lower likelihood of subsequent delinquent behavior.