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Author: Park, Hyejoon
Resulting in 1 citation.
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Guo, Siying Liu, Jianxuan Meng, Chen Park, Hyejoon |
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Longitudinal Impacts of Religious Profiles on Substance Abuse Among Emerging Adults: A Fusion of Unsupervised and Supervised Learning Approach Deviant Behavior published online (05 Sep 2023). Also: https://doi.org/10.1080/01639625.2023.2254904 Cohort(s): NLSY97 Publisher: Taylor & Francis Keyword(s): Drug Use; Learning, Supervised (Machine Learning/AI); Learning, Unsupervised (Machine Learning/AI); Machine Learning; Religion; Religious Attendance; Religious Beliefs; Substance Use; Young Adults This study aims to assess the longitudinal patterns of multifaceted religious profiles and their relationships with illegal substance abuse among young people transitioning from late adolescence to early adulthood. A novel longitudinal approach integrating the cutting-edge unsupervised and supervised learning techniques is proposed to analyze the data from the National Longitudinal Survey of Youth 1997. The results show that emerging adults who are highly religious in either subjective (e.g., religious beliefs) or objective (e.g., religious attendance) domain are much less likely to abuse illegal substances than their religiously disengaged peers. Religiosity, regardless of subjective or objective, tends to be protective, but its effect is most prominent among young people most profoundly devoted to both religious beliefs and behaviors. Nevertheless, possessing strong commitment to religious beliefs without accompanying frequent religious behaviors may put emerging adults at greater risk for illicit substance abuse, compared to those who hold high level of religious beliefs but do not engage in corresponding religious behaviors frequently. |
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Bibliography Citation
Guo, Siying, Jianxuan Liu, Chen Meng and Hyejoon Park. "Longitudinal Impacts of Religious Profiles on Substance Abuse Among Emerging Adults: A Fusion of Unsupervised and Supervised Learning Approach." Deviant Behavior published online (05 Sep 2023).
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