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Browsing by Author "Hong, Saahoon"
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ItemAnalysis 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. ItemBehavioral 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. ItemDoes 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. ItemExamining 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. ItemIntersection 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. ItemThe 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. ItemLongitudinal 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. ItemManaging 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. ItemMediational Effect of Teacher-Based Discrimination on Academic Performance: An Intersectional Analysis of Race, Gender, and Income/Class(MDPI, 2023-04) Kyere, Eric; Hong, Saahoon; Gentle-Genitty, Carolyn; School of Social WorkDrawing on prior research, this study applies an intersectional framework to investigate discrimination in the context of teacher–student relationships and its influence on students’ academic outcomes. Outcomes assessed were inclusive of self-efficacy, school attendance, and grade point average (GPA). For this analysis, structural equation modeling was used with a cross-sectional sample of the Maryland and Adolescent Development in Context Study (MADICS) and the youth self-administered (YSA) questionnaires administered when the youth were in 8th grade (Wave 3). A total of 1182 students completed the survey, of whom 704 were selected for this study. Findings show teacher discrimination as a mechanism to uncover some of the ways race, gender, and income simultaneously intersect to affect students’ academic outcomes. The current study confirms and extends prior work establishing associations among race, gender, income, and teacher discrimination and academic outcomes among African American youth. African American students, especially males, regardless of income levels, may benefit directly—evidenced in visible academic performance—from more positive and race-conscious interactions with teachers. Future implications for practice are shared. ItemPredicted mental illness of Asian-American amid the COVID-19 pandemic & approaches to treatment(2021-05-19) Hong, Saahoon; Walton, Betty A.Given that jointly experiencing racial violence and harassment and pandemic-related hardship can worsen mental health state, this presentation planned to discuss patterns and changes in Asian-Americans’ behavioral health during the pandemic. Specifically, a machine learning algorithm with the statewide-secondary administrative data was applied to identify the associations among race-related predictors, behavioral health needs, and behavioral functioning needs.