Browsing by Author "Hong, Saahoon"
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Item Analysis of mothers’ perceptions affecting eating habits of young children with/without disabilities: A machine learning method(Kyobo, 2022) Park, So-young; Hong, Saahoon; Yoon, Cynthia; School of Social WorkThe purpose of this study was to confirm the mothers perceptions of the influence of eating habits of young children with/without disabilities. Through a survey study, the intersection between factors affecting understanding of baby food, practice of weaning food, and children's current eating habits was analyzed using a machine learning-based decision tree approach. Results indicate that, first, there was a significant difference in understanding of weaning foods between mothers of children with and without disabilities. The late timing of weaning foods was associated with an unbalanced diet and overeating. Second, there was a significant difference in the breastfeeding pattern before transitioning to baby foods in mothers of disabled infants and non-disabled infants. Before weaning, mothers of infants with disabilities were more likely to feed formula or a mixture of milk and formula. Third, the mother's job status during the weaning period showed a significant intersection with the current number of snacks, the preparation of weaning food, and the types of preferred snacks of the disabled infants. Discussion includes the need for diet education and related supports systematically for mothers of infants/children with disabilities.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 Does a Drop-in and Case Management Model Improve Outcomes for Young Adults Experiencing Homelessness: A Case Study of YouthLink(University of Minnesota, 2022-03) Foldes, Steven S.; Long, Kirsten Hall; Piescher, Kristine; Warburton, Katelyn; Hong, Saahoon; Alesci, Nina L.This study used two approaches to examine YouthLink as an example of a drop-in and case management model for working with youth experiencing homelessness. These approaches investigated the same group of 1,229 unaccompanied youth, ages 16 to 24 and overwhelmingly Black, who voluntarily visited or received services from YouthLink in 2011. Both approaches looked at the same metrics of success over the same time period, 2011 to 2016. One approach—Study Aim 1—examined the drop-in and case management model overall, asking whether YouthLink’s service model resulted in better outcomes. It compared a YouthLink cohort with a group of highly similar youth who did not visit YouthLink but may have received similar services elsewhere. A second approach—Study Aim 2—investigated within the YouthLink cohort the ways in which YouthLink’s drop-in and case-management approach worked toward achieving the desired outcomes. The results and their implications were discussed.Item 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-WonTo 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.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 Intersection of Disability, School Climate, and School Violence in Inclusive Settings(2023-01-13) Hong, SaahoonGiven that few studies have examined the intersectionality of bullying, disability, and self-efficacy, this study highlighted the intersection of disability, psychosocial characteristics, school violence, friendship, and teacher roles in examining the effect of school violence and school climate on self-efficacy among students with disabilities.Item The intersectionality of gambling addiction recovery and mental illness: A machine learning approach(Society for Social Work and Research 26th Annual Conference, 202-01-15) Hong, Saahoon; Walton, Betty A.; Kim, Hea-WonA machine learning algorithm identified that struggling with substance use, impulse control, education, and resourcefulness was the significant barriers to improvement from problem gambling in state-funded behavioral health services. Interestingly, White adults were more likely to be improved from problem gambling than their peers of color. The machine learning-based gambling addiction recovery model could be a promising approach to detect the intersection of race/ethnicity, behavioral health challenges, and their improvement from problem gambling. It could eventually be a basis for developing a gambling addiction recovery model for adults with needs for gambling addiction treatment at the initial assessment. Such a relationship study will support the development of an efficient mental health and gambling recovery model.Item Longitudinal Patterns of Strengths Among Youth with Psychiatric Disorders: A Latent Profile Transition Analysis(Springer, 2021-07-13) Hong, Saahoon; Walton, Betty A.; Kim, Hea-Won; Lee, Sunkyung; Rhee, TaehoA better understanding of variability in the strengths of youth with psychiatric disorders is critical as a strength-based approach can lead to recovery. This study aimed to identify subgroups of strengths among youth with mental disorders and determine whether subgroups changes were associated with mental health recovery. Youth with mental disorders (N = 2228) from a statewide database were identified in the state fiscal year of 2019. Using the latent profile analysis and latent transition analysis, we identified three strength profiles (i.e., essential, usable, and buildable). Over 90% of youth sustained or developed strengths over time. Positive transitions were associated with mental health recovery, symptom reduction, and personal recovery. Buildable strengths supported youth’s personal recovery independent of improving mental health needs. The findings suggest that subgroups of strengths may be a promising source for planning and tracking youth’s progress and guiding clinicians to more efficiently allocate community-based resources.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.