Browsing by Author "Walton, Betty"
Now showing 1 - 10 of 15
Results Per Page
Sort Options
Item Behavioral Health Needs of Older Adults Living in Poverty: Machine Learning-Based Predictive Models(2023-01-13) Hong, Saahoon; Yi, Eun-Hye G.; Walton, Betty; Kim, Hea-WonTo develop contextually sensitive and effective services for older adults in poverty, this study aimed to identify the characteristics and patterns of older adults’ BH service needs, compared to those of middle-aged adults. The findings suggest that employment is the most important predictor for classifying older adults with behavioral health needs, followed by adjustment to trauma, independent living, legal system involvement, sleep, disability, transportation, social skills, and self-care. Interestingly, gender and race were not significantly important in classifying behavioral health needs between middle-aged and older adult groups. The older adults who had non-actionable ratings on employment and actionable ratings on the legal system (current JS involvement), middle-aged adults were more likely to struggle with anxiety than older adults. The older adults with non-actionable ratings on employment, legal system, and adjustment to trauma, non-disabled older adults were more likely to present behavioral health needs on medical/physical, anxiety, independent living, recreational, and sleep.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 Collaborate, Review Data and Change; Repeat(2022-09-27) Walton, Betty; Wendy, HarroldContext. The ANSA and TCOM framework, in conjunction with related information, can support data-informed policy planning and funding initiatives. One state’s collaborative data-informed recovery strategies provide an example. The concept of recovery from mental health and substance use disorders evolved from a deficit focus to include functional and personal recovery. Through a collaborative process SAMHSA, the federal behavior health authority, developed recovery’s working definition: “a process of change through which individuals improve their health and wellness, live a self- directed life, and strive to reach their full potential” in four dimensions (Health - overcoming or managing one’s diseases or symptoms, Home – a stable and safe place to live, Purpose - meaningful daily activities, and Community - relationships and social networks that provide support, friendship, love, and hope. Recently, SAMHSA acknowledged that the 2012 framework needs to evolve and issued a Recovery Challenge to community-based organizations to highlight innovative recovery strategies and practices. The challenge requires active, meaningful involvement of individuals with lived experience. Recovery and TCOM frameworks were cross walked to support training and to inform the state’s recovery support strategies. One strategy was to create a Recovery Support Workgroup (RSW) comprised of a dozen state agencies and community stakeholders. More than 51% of RSW members have lived experience with mental health and/or substance use. This group makes data-informed recommendations to state’s Mental Health and Addiction Planning and Advisory Council. Following a statewide 2019 gap analysis, which identified recovery support needs, the RSW created subgroups to address five recovery support needs: Personal support networks, Peer support services, Hobbies and interests, Prevention and wellness, and Safe and affordable housing. Data Collection Methodology. Multiple data sources informed the subgroups’ efforts: a consumer satisfaction survey, social determinants of health survey a Lived Experience Survey distributed through recovery groups, Medicaid claims diagnoses, recovery data collected at recovery organizations, and ANSA data. All data were associated with recovery dimensions: Health, Home, Community and Purpose. For ANSA data, the process required collaboration among the state’s data management, recovery support services, and the IU CANS/ANSA technical assistance teams. Enhancing ANSA Outcome Management Reports. Outcome Management reports, available to the state team, providers, and the IU CANS/ANSA team, were modified to inform the RSW subgroups by formatting reports by recovery dimensions and additional concerns. Building on existing reports (Resolved Actionable Needs, and item level metrics (Actionable, Continuing, Clinical Progress, Newly Identified, and Worsening), three new recovery focused reports were developed. Sharing and using the data. This collaborative, data-informed recovery initiative has received national attention. A variety of strategies to disseminate and to use the results for planning and managing change will be discussed: What has worked? What has been challenging? What has not work? What are the implications for quality improvement, program evaluation, and research?Item Deliberate Self-Harm in Young Children(2020-08) Lewis, Lisa McConnell; Adamek, Margaret E.; Vernon, Robert; Aalsma, Matthew C.; Walton, BettyWhile deliberate self-harm (DSH) in adolescents and adults has been established as a reliable predictor of future suicidal behavior and attempts, whether the same is true for younger children has rarely been studied. Two separate articles will address issues regarding intentional self-injury in young children. The first identified describes the demographic profile of young children who engage in NSSI and evaluated whether predictors of adolescent NSSI are also associated with NSSI in children. The second manuscript analyzed NSSI behaviors to see if they can be correctly predicted from knowledge of a child's history of maltreatment to identify which trauma variables are central in prediction of NSSI status. A Chi-square and logistic regression were run on data from 16,271 records of children ages 5-9 years who received services from the IDMHA in 2018. NSSI was significantly (p < .000) associated with trauma history (x2 = 75.54, df = 1), anxiety (x2 = 107.59, df = 1), depression (x2 = 217.011, df = 1), suicide risk (x2= 993, df = 1), and impulsivity (x2 = 122.49, df = 1. Presence of a caregiver mental health problem (x2 =38.29, df = 1), age (x2 = 14.18, df = 4), being male (x2 = 11.59, df = 1), and being Caucasian (x2 = 23.29, df = 6) at p < .05. Regression results indicated the overall model of seven predictors (sexual abuse [OR 1.14], physical abuse [OR 1.26], emotional abuse [OR1.3], neglect [OR .895], medical trauma [OR 1.34], exposure to natural disaster [OR 1.81] and victim of a crime [1.14] was statistically reliable in distinguishing between children who self-injure and those who do not. [-2 Log Likelihood = 6228.78, x2(6) = 105.416, p < .000]. NSSI does occur in preadolescent children and while there is some indication that the risk factors and co-variates are like those of adolescents, there are some differences which need further study. Training clinicians to inquire about self-injury during assessment of younger children is a simple step. The variables of age and sex throughout development as well as identifying protective as well as risk factors with children should be studied.Item Examining the emergence of a learning collaborative: a framework to support complex program implementation(Springer Nature, 2024-04) Karikari, Isaac; Walton, Betty; Ludeker-Seibert, Kristen; Riley, Kathy; School of Social WorkTo address unmet behavioral health needs of children and youth, the system of care (SOC) philosophy was developed and evolved into a framework to support community-based coordinated networks that provide an array of effective services and supports. There is limited elaboration of the actual implementation processes and intricacies of SOC development, particularly, in terms of the roles of local SOC coordinators (local coordinators). Limited published research has addressed the necessary knowledge and roles of local coordinators, and the theoretical underpinnings and structure of their learning, skill development, and capacity building. Utilizing an archival approach and reflexive thematic analysis, this qualitative study examined records of three forums involving 50 local SOC coordinators (11% male, 89% female, 93% white, ages between 27 and 66 years) between 2017 and 2018. The analysis revealed varying levels of experience, knowledge, and skills, and uncovered several SOC development strategies utilized by coordinators. The study illustrates the inception of a learning collaborative that served as a bridge and implementation driver for SOC development and socio-professional support for local coordinators. The findings provide an empirical base and emerging framework for SOC coordinators’ training and professional development. The value of learning collaboratives in facilitating exposure to a diverse knowledge base and the importance of fostering supportive spaces for coordinators as they strive to develop SOCs are evident. Incorporating supportive learning collaboratives for local change agents could be a dynamic strategy to support the effective implementation of system-wide changes or enhancements in behavioral health services.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 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 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 Managing Recovery with Adults Involved in Behavioral Health and Criminal Justice Systems(2022-09-21) Hong, Saahoon; Walton, BettyYoung adults with mental health needs experience increased criminal behaviors, peaking at 16-25 years. In addition, the lack of support for young adults' behavioral health needs increases the likelihood of further involvement in the justice system. This study aimed to predict dual behavioral health and justice system involvement for adults participating in publicly funded treatment and support services needs. Policy implications were also discussed.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.