Browsing by Author "Kim, Hea-Won"

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    ANSA: Becoming a Recovery Focused Tool
    (2014-11) Walton, Betty A.; Kim, Hea-Won
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    ANSA: Becoming a Recovery Focused Tool
    (Office of the Vice Chancellor for Research, 2013-04-05) Walton, Betty A.; Kim, Hea-Won; Park, SeonHye
    The Adult Needs and Strength Assessment (ANSA, Lyons, 2009) has been used across public mental health and addiction services in Indiana to help develop intervention plans and to monitor client progress. ANSA consists of six core domains (Life Functioning, Behavioral Health Needs, Risk Behaviors, Strengths, Acculturation, and Caregiver). Domain items are rated on a four-point scale to describe the degree to which a need interferes with functioning or a useful strength is present. Despite statewide implementation, literature related to the ANSA is scarce. The study evaluates the psychometric properties of ANSA and its role as an outcome performance measure. Adults for whom the ANSA had been rated at four points between 2008 and 2010 were included (N=6320). Internal consistency reliability was measured for each ANSA domain and outcome measure. Reliable change indices (RCI) for each domain were used to calculate significant change. At each point of assessment and across time, the Cronbach’s alphas for all domains, except Risk Behaviors, are in the acceptable to high ranges (0.71 to 0.92), indicating good internal consistency and stability. For outcome performance measures, a more realistic timeframe for assessments (12 months) was required to document reliable improvement in at least one ANSA domain for individuals with serious mental health needs. The Residential Stability outcome measure has the low internal consistency and stability. From the recovery perspective, a new Community Integration measure was proposed as an alternative outcome measure and proved to be reliable (α = .90). Study findings helped enhance the ANSA tool, create a new outcome measure, and inform state policy. Specifically, bridging research to practice, findings resulted in restructuring the ANSA Risk Domain and modifying how outcomes are measured for adults in recovery focused behavioral health services.
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    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-Won
    To 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.
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    Civic Engagement among Middle Eastern and North African Refugees and Immigrants
    (62nd Annual Program Meeting, Atlanta, 2016. Atlanta, GA: Council on Social Work Education, 2016-11) Makki Alamdari, Sara; Alhajeri, Wafa; Kim, Hea-Won
    This research explored the attitudes toward, frequency and predictors of civic engagement among the Middle East and North Africa (MENA) immigrants and refugees. Respondents (n=106) reported strongly positive attitudes and engaged in various civic activities. Attitudes were found as main predictor for level of civic engagement.
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    Co-occurring Substance Use and Mental Health Needs: Enhancing the Adult Needs and Strengths Assessment (ANSA) to Manage Services
    (2019-10-04) Walton, Betty A.; Kim, Hea-Won
    Substance use disorders (SUD) are common, affecting one in 25 adolescents (ages 12 -17), one in seven young adults (ages 18 to 25), and one in 16 adults (ages 26 and older) during 2017. 1 While 16.7% of adults without SUD experienced mental health (MH) disorders, 45.6% of adults with SUD experienced co-occurring MH disorders.1 Related research found much higher rates of adults with MH or SUD disorders (50-75%) have co-occurring disorders. 2, 3, 4, 5 Co-occurring MH and SUD make treatment more difficult, increase use of health resources, and interfere with individuals’ life functioning.2, 3, 4 In response to the opioid crisis, SUD treatment funding and services are expanding. Effective treatment requires identification of co-occurring disorders (COD). The goal of this study was to examine how well practitioners assess and identify COD in practice.
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    Cross-border fathering: The lived experience of Mexican immigrant fathers
    (2009-01-16T17:14:33Z) Navarro, Daniel E.; Sullivan, William P.; Kim, Hea-Won; Queiro-Tajalli, Irene; Horton-Deutsch, Sara
    This phenomenological study explores the lived experience of Mexican immigrant fathers who migrate to and settle in the United States initially alone and eventually bring the rest of their families from Mexico to join them permanently. This project explores fathers’ understanding of their fathering efforts along the journey of migration; from departure from Mexico to family resettlement in the U.S. There is a conspicuous paucity of research focusing on the fathering experience among these men. In addition, negative stereotypes about the Mexican men in general abound. Thus, this study clarifies and contributes to the existing knowledge about these men. Fifteen Mexican immigrant fathers participated in the study through extensive qualitative interviews and field observation. Interviews were carried out in Spanish, audio taped, and simultaneously translated and transcribed into English. Data were treated through the process of phenomenological reduction. Nine core themes emerged: (1) fathers immigrate to rescue their families from poverty and fulfill what they perceive to be their roles as breadwinners; (2) they could not embark upon this journey without the support of family and kin in both countries; (3) they sacrifice themselves and their families as well; (4) despite the geographical distance, their fathering efforts involve much more than providing for their children; (5) they vow to ensure that neither they nor their families would ever experience certain risks again; (6) once in the U.S., they experience a type of poverty they did not anticipate; (7) due to immigration policy, the border is never left behind; (8) the role of the wife is significant throughout the father’s experience; and (9) despite the challenges experienced, fathers recognize and appreciate the gains from their decisions to engage in cross-border fathering. The essence of the phenomenon involves the recognition that although the Mexico - U.S. border is left behind after crossing the border, the father never stops crossing familial, social, and psychological borders. As a triangulation strategy, five professionals with significant experience working with Mexican immigrant families were also interviewed. Implications for practice, education, research, and policy are identified and discussed. Questions about the future of this population group are raised.
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    Examining the intersection of mental illness and suicidal risk in the shadow of a pandemic: A Machine Learning Approach
    (2021-10-08) Hong, Saahoon; Walton, Betty A.; Kim, Hea-Won
    To develop the suicidal recovery model for adults with mental illness during the pandemic and better serve them in the mental health system, it is necessary to ensure that we can identify the intersection of mental illness and suicidal risk. Therefore, we used machine learning to examine the intersection of mental illness and suicide aged 17 years old and above adults in the Mideastern state-funded mental health service (n=29,267) during the calendar years of 2019 and 2020. Classification, regression tree analyses, and chi-square automatic interaction detection (CHAID) were used to identify the intersection of mental illness and suicidal risk and determine their classification accuracy. In the COVID-19 pandemic year, self-injurious behavior, depression, adjustment to trauma, danger to others, impulse control, anger control, age, sleep, and psychosis were identified as the critical factors associated with suicidal risk. However, danger to others, impulse control, anger control, and age were associated with suicide risk only in 2020, but not in 2019. Overall, self-injurious behavior, depression, danger to others, psychosis, adjustment to trauma, anxiety, sleep, and interpersonal were intersected with suicidal risk.
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    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, Stephanie
    This 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.
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    Gregory Research Beliefs Scale: Factor Structure and Psychometric Properties
    (2009-06-23T21:31:07Z) Gregory, Virgil L., Jr; Pike, Cathy K.; Adamek, Margaret E.; Kim, Hea-Won; Appleby, Drew C.
    GREGORY RESEARCH BELIEFS SCALE: FACTOR STRUCTURE AND PSYCHOMETRIC PROPERTIES The study at hand involves developing the Gregory Research Beliefs Scale (GRBS) to reliably and validly measure social work students’ beliefs about the function of research in social work practice. Research has considerable actual and potential benefits for practice. Social work students’ beliefs about this construct are vital. A description of the advantages of using research to inform practice is given. Additionally, the Council on Social Work Education and National Association of Social Workers’ policies that mandate the merger of research and practice are also provided to further justify the need for adequate psychometric evaluation of the construct. Details of the literature search strategy are described and critical evaluations of the empirical articles are conducted. Based on critical evaluations of instruments which have previously measured the same construct, a number of psychometric shortcomings are outlined to validate the need for further scale development of the construct. The present study’s objectives were to develop a scale which has an empirically and theoretically supported factor structure, acceptable coefficient alpha levels, empirically supported discriminant (divergent) validity, concurrent criterion validity, and known–groups criterion validity. Steps for developing the GRBS’s items, response format, sample, research design, and statistical tests are specified and conducted to determine the factor structure and psychometric properties. Finally, the strengths, limitations, and areas for future research are discussed.
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    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, Stephanie
    Substance 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.