Hyejoon Park

Bio: Hyejoon Park is an academic researcher from Pittsburg State University. The author has contributed to research in topics: Mental health & Psychological resilience. The author has an hindex of 6, co-authored 16 publications receiving 110 citations. Previous affiliations of Hyejoon Park include University of Illinois at Urbana–Champaign & Western Michigan University.

Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.

Abstract: The current study examined the predictive ability of discrimination-related variables, coping mechanisms, and sociodemographic factors on the psychological distress level of Korean immigrants in the US amid the COVID-19 pandemic Korean immigrants (both foreign-born and US-born) in the US above the age of 18 were invited to participate in an online survey through purposive sampling In order to verify the variables predicting the level of psychological distress on the final sample from 42 states (n = 790), the Artificial Neural Network (ANN) analysis, which is able to examine complex non-linear interactions among variables, was conducted The most critical predicting variables in the neural network were a person's resilience, experiences of everyday discrimination, and perception that racial discrimination toward Asians has increased in the US since the beginning of the COVID-19 pandemic

Challenges and coping strategies of East Asian graduate students in the United States

Abstract: This study uses qualitative analysis of semi-structured interviews to explore the adjustment challenges encountered by international graduate students during their study at US universities, and the protective factors most associated with successful adjustment. Nine students from China, Korea, and Taiwan, attending a Midwestern university, participated in the study. Language barriers and discrimination were the primary challenges reported; the factors most associated with successful adjustment were personal perception, social support, strong mentoring relationships, religious belief, and use of campus services. Implications for providing effective assistance for international graduate students are discussed.

Coronavirus disease (COVID-19) cases analysis using machine-learning applications.

Abstract: Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

Clinical outcomes and risk factors for COVID-19 among migrant populations in high-income countries: a systematic review.

Abstract: Background: Migrants in high-income countries may be at increased risk of COVID-19 due to their health and social circumstances, yet the extent to which they are affected and their predisposing risk factors are not clearly understood. We did a systematic review to assess clinical outcomes of COVID-19 in migrant populations, indirect health and social impacts, and to determine key risk factors. Methods: We did a systematic review following PRISMA guidelines (PROSPERO CRD42020222135). We searched multiple databases to 18/11/2020 for peer-reviewed and grey literature on migrants (foreign-born) and COVID-19 in 82 high-income countries. We used our international networks to source national datasets and grey literature. Data were extracted on primary outcomes (cases, hospitalisations, deaths) and we evaluated secondary outcomes on indirect health and social impacts and risk factors using narrative synthesis. Results: 3016 data sources were screened with 158 from 15 countries included in the analysis (35 data sources for primary outcomes: cases [21], hospitalisations [4]; deaths [15]; 123 for secondary outcomes). We found that migrants are at increased risk of infection and are disproportionately represented among COVID-19 cases. Available datasets suggest a similarly disproportionate representation of migrants in reported COVID-19 deaths, as well as increased all-cause mortality in migrants in some countries in 2020. Undocumented migrants, migrant health and care workers, and migrants housed in camps have been especially affected. Migrants experience risk factors including high-risk occupations, overcrowded accommodation, and barriers to healthcare including inadequate information, language barriers, and reduced entitlement. Conclusions: Migrants in high-income countries are at high risk of exposure to, and infection with, COVID-19. These data are of immediate relevance to national public health and policy responses to the pandemic. Robust data on testing uptake and clinical outcomes in migrants, and barriers and facilitators to COVID-19 vaccination, are urgently needed, alongside strengthening engagement with diverse migrant groups.

Administration and Policy in Mental Health and Mental Health Services Research

Abstract: Administration and Policy in Mental Health and Mental Health Services Research aims to improve the effectiveness of mental health and related human service programs by advancing research on services and the practice and process of administration in the mental health setting. We welcome studies that are conducted and reported according to well-accepted guidelines in the research community, such as the CONSORT statement (randomized controlled trials), the PRISMA statement (systematic reviews and meta-analyses), STROBE (observational studies), SRQR (qualitative research) and CARE (case reports).

Segmented assimilation on the ground: the new second generation in early adulthood

Abstract: Abstract We review the literature on segmented assimilation and alternative theoretical models on the adaptation of the second generation ; summarize the theoretical framework developed in the course of the Children of Immigrants Longitudinal Study [CILS]; and present evidence from its third survey in South Florida bearing on alternative hypotheses. We find that the majority of second-generation youths are moving ahead educationally and occupationally, but that a significant minority is being left behind. The latter group is not distributed randomly across nationalities, but corresponds closely to predictions based on immigrant parents’ human capital, family type, and modes of incorporation. While it is clear that members of the second generation , whether successful or unsuccessful will assimilate – in the sense of learning English and American culture – it makes a great deal of difference whether they do so by joining the mainstream middle-class or the marginalized, and largely racialized, population at the bottom. Narratives drawn from the ethnographic module accompanying the survey put into perspective quantitative results and highlight the realities of segmented assimilation as it takes place today in U.S. society.

Extracurricular Activities and Disadvantaged Youth: A Complicated—But Promising—Story:

Abstract: Increased political and research interest in extracurricular activities stems, in part, from the claim that these programs especially benefit disadvantaged youth. However, little literature has syn...