Patterns of Online Activities and Related Psychosocial Factors in Adolescence: a Latent Class Analysis.

Special attention should be paid to the types of online activities in which adolescents engage, along with frequencies of the activities. Thus, we aimed to identify homogeneous subgroups of adolescents using five online activities and to examine differences in 12 characteristics across the subgroups...

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Publicado en:International Journal of Mental Health & Addiction Vol. 17; no. 5; pp. 1147 - 1162
Autores principales: Park, Sunhee, Lee, Haein
Formato: Artículo
Publicado: Springer Nature Oct2019
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2019
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      pub: Springer Nature
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        139137846
        10.1007/s11469-018-0035-1
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        atl: Patterns of Online Activities and Related Psychosocial Factors in Adolescence: a Latent Class Analysis.
      aug:
        au:
          Park, Sunhee
          Lee, Haein
        affil:
          College of Nursing Science, East-West Nursing Research Institute, Kyung Hee University, 26 Kyunghee-daero, Dongdaemun-gu, 02447, Seoul, Republic of Korea
          College of Nursing, Research Institute of Nursing Science, Catholic University of Daegu, 33 Duryugongwon-ro 17-gil, Nam-gu, 42472, Daegu, Republic of Korea
      su:
        South Korea
        Psychosocial factors
        Adolescence
        Time management
        Computer surveys
        Secondary analysis
      sug:
        subj:
          Psychosocial factors
          Adolescence
          Time management
          South Korea
          Computer surveys
          Secondary analysis
      keyword:
        Adolescents
        Internet use
        Korean Children and Youth Panel Survey
        Latent class analysis
        Online activities
        Typology of Internet users
        Adolescents
        Internet use
        Korean Children and Youth Panel Survey
        Latent class analysis
        Online activities
        Typology of Internet users
      ab: Special attention should be paid to the types of online activities in which adolescents engage, along with frequencies of the activities. Thus, we aimed to identify homogeneous subgroups of adolescents using five online activities and to examine differences in 12 characteristics across the subgroups we identified. We cross-sectionally analyzed nationally representative secondary data collected in Korea (N = 1827 adolescents who used a computer at the time of the survey). We performed latent class analysis, which is a person-centered approach, to understand the patterns of online activities in adolescence. A three-latent-class model best fit the data: frequent use for academics (FUA), non-frequent use for all activities (NFUAA), and frequent use for multiple activities (FUMA). In general, the FUA class differed from the other two classes. Specifically, the FUA class spent less time online and had better psychosocial conditions. Given these findings, health professionals should implement strategies aimed at assisting (a) adolescents to be aware that some types of online activities can potentially pose a risk to their psychosocial conditions and (b) parents to actively participate in guiding children's Internet use.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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