Predicting and explaining recurrent child abuse using interpretable machine learning: Evidence from national-level child abuse data in South Korea (2017–2020)
Dong-Hun Kim, Ting Jiang, Kihyun Kim, Jae-Won Kim, Yongjun Zhu
Predictive model for intra-familial child maltreatment re-reports and recurrence in South Korea: Analysis of national child protection services case records
Kihyun Kim, Jungtae Choi, Heesun Jang, Hyun Ji Lee, Hwajung Jang
Predictive Risk Modeling for Recurrence of Child Maltreatment Using Cases from the National Child Maltreatment Data System in Korea: Exploratory Data Analysis Using Data Mining Algorithm
Predicting and explaining recurrent child abuse using interpretable machine learning: Evidence from national-level child abuse data in South Korea (2017–2020)
Dong-Hun Kim, Ting Jiang, Kihyun Kim, Jae-Won Kim, Yongjun Zhu
Predictive model for intra-familial child maltreatment re-reports and recurrence in South Korea: Analysis of national child protection services case records
Kihyun Kim, Jungtae Choi, Heesun Jang, Hyun Ji Lee, Hwajung Jang
Predictive Risk Modeling for Recurrence of Child Maltreatment Using Cases from the National Child Maltreatment Data System in Korea: Exploratory Data Analysis Using Data Mining Algorithm
Sexual Victimization and Psychological and Behavioral Outcomes Among Children and Adolescents in South Korea
Hyun Ji Lee, Jungtae Choi, Jae-Won Kim, Kihyun Kim
IF 1.8
Psychiatry Investigation
These findings highlight the importance of examining experiences of sexual violence in a multifaceted manner. This approach may provide more effective interventions for survivors and allow clinicians to gain an in-depth understanding of sexual victimization in children and adolescents while also increasing the understanding of potential psycho-behavioral consequences.
Interpretable machine learning‐based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data
Donghun Kim, Lihong Quan, Mihye Seo, Kihyun Kim, Jae‐Won Kim, Yongjun Zhu
IF 2.6
Suicide and Life-Threatening Behavior
Our findings can help governmental organizations to better assess female suicide risk in South Korea and develop more informed and customized suicide prevention strategies.
The Relationship of Risky Online Behaviors and Adverse Childhood Experiences to Online Sexual Victimization Among Korean Female Adolescents
Jungtae Choi, Mihye Seo, Jaewon Kim, Kihyun Kim
IF 2.3
Journal of Interpersonal Violence
Prior research has demonstrated that online sexual victimization (OSV) is a significant social problem and is associated with adolescents' negative developmental outcomes. However, it remains unclear whether adolescents' risky online behaviors and offline victimization are related to the risk of OSV. The present study examined whether female adolescents' risky online behaviors (mood regulation through the Internet, ingratiating behavior, disclosure of personal information, harassing behavior, talking with someone met online, and sexual behavior) and offline victimization (adverse childhood experiences [ACEs]) would be associated with OSV. This study recruited female adolescents and their mothers within six metropolitan cities and provinces of residential areas of South Korea. A total of 509 female adolescents participated in the survey (aged 13-18 years). The present study employed multivariate regression to examine the relationship of risky online behaviors and offline victimization to the experience of OSV. Female adolescents' risky online behaviors (harassing behavior, talking with someone met online, and sexual behavior) were significantly associated with OSV, and those with high exposure to maltreatment and family dysfunction during childhood were more at risk of OSV than adolescents with low exposure to ACEs. The results suggest that it is important to address the effects of risky online behaviors and exposure to offline victimization on female adolescents' sexual victimization online. Identifying risky online behaviors and offline victimization related to OSV can help researchers and practitioners further understand female adolescents' online victimizations in the context of offline and online dynamics.
