IDEAS home Printed from https://ideas.repec.org/a/spr/chinre/v16y2023i4d10.1007_s12187-023-10036-y.html
   My bibliography  Save this article

Adverse Childhood Experiences and Substance Use Among Korean College Students: Different by Gender?

Author

Listed:
  • Aely Park

    (Sunchon National University)

  • Youngmi Kim

    (Virginia Commonwealth University)

  • Jennifer Murphy

    (Virginia Commonwealth University)

Abstract

Research has shown that adverse childhood experiences (ACEs) lead to substance use and this relationship may differ by gender. There is limited knowledge about how ACEs relate to the risk of substance use in South Korea. The current study aims to identify the latent patterns of co-occurring ACEs and examines their relationships with substance use among college students. This study used data collected in 2019 from a national sample of Korean college students (N = 1,037). This study conducted a Latent Class Analysis (LCA) with distal outcomes, using the Bolck, Croons, and Hagenaars method. The independent variable was the patterns of ACEs identified using 14 indicators of childhood adversity. Two dependent variables measured self-reported substance use in smoking and alcohol use. The LCA identified four heterogeneous ACEs classes: High Adversity, Family Violence, Economic Hardship, and Low Adversity. The analysis showed different associations between the ACE patterns and substance use by gender: For women, the Family Violence classes (OR = 1.13, p

Suggested Citation

  • Aely Park & Youngmi Kim & Jennifer Murphy, 2023. "Adverse Childhood Experiences and Substance Use Among Korean College Students: Different by Gender?," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(4), pages 1811-1825, August.
    • Handle: RePEc:spr:chinre:v:16:y:2023:i:4:d:10.1007_s12187-023-10036-y
    DOI: 10.1007/s12187-023-10036-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12187-023-10036-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12187-023-10036-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Pilowsky, D.J. & Keyes, K.M. & Hasin, D.S., 2009. "Adverse childhood events and lifetime alcohol dependence," American Journal of Public Health, American Public Health Association, vol. 99(2), pages 258-263.
    3. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
    4. Leban, Lindsay & Gibson, Chris L., 2020. "The role of gender in the relationship between adverse childhood experiences and delinquency and substance use in adolescence," Journal of Criminal Justice, Elsevier, vol. 66(C).
    5. Lee, Hye Yeon & Kim, Isak & Nam, Sojeong & Jeong, Jeongwoon, 2020. "Adverse childhood experiences and the associations with depression and anxiety in adolescents," Children and Youth Services Review, Elsevier, vol. 111(C).
    6. Metzler, Marilyn & Merrick, Melissa T. & Klevens, Joanne & Ports, Katie A. & Ford, Derek C., 2017. "Adverse childhood experiences and life opportunities: Shifting the narrative," Children and Youth Services Review, Elsevier, vol. 72(C), pages 141-149.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jennifer Oser & Marc Hooghe & Zsuzsa Bakk & Roberto Mari, 2023. "Changing citizenship norms among adolescents, 1999-2009-2016: A two-step latent class approach with measurement equivalence testing," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4915-4933, October.
    2. Bakk, Zsuzsa & Kuha, Jouni, 2020. "Relating latent class membership to external variables: an overview," LSE Research Online Documents on Economics 107564, London School of Economics and Political Science, LSE Library.
    3. Gugerty, Mary Kay & Mitchell, George E. & Santamarina, Francisco J., 2021. "Discourses of evaluation: Institutional logics and organizational practices among international development agencies," World Development, Elsevier, vol. 146(C).
    4. Meldrum, Ryan C. & Stults, Brian J. & Hay, Carter & Kernsmith, Poco D. & Smith-Darden, Joanne P., 2022. "Adverse childhood experiences, developmental differences in impulse control and sensation seeking, and delinquency: A prospective multi-cohort study," Journal of Criminal Justice, Elsevier, vol. 82(C).
    5. Sara N. Naicker & Marilyn N. Ahun & Sahba Besharati & Shane A. Norris & Massimiliano Orri & Linda M. Richter, 2022. "The Long-Term Health and Human Capital Consequences of Adverse Childhood Experiences in the Birth to Thirty Cohort: Single, Cumulative, and Clustered Adversity," IJERPH, MDPI, vol. 19(3), pages 1-19, February.
    6. Aurélie Bertrand & Christian Hafner, 2014. "On heterogeneous latent class models with applications to the analysis of rating scores," Computational Statistics, Springer, vol. 29(1), pages 307-330, February.
    7. Yajing Zhu & Fiona Steele & Irini Moustaki, 2020. "A multilevel structural equation model for the interrelationships between multiple latent dimensions of childhood socio‐economic circumstances, partnership transitions and mid‐life health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1029-1050, June.
    8. Jason V. Chen & Kurt H. Gee & Jed J. Neilson, 2021. "Disclosure Prominence and the Quality of Non‐GAAP Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 59(1), pages 163-213, March.
    9. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    10. Brewsaugh, Katrina & Masyn, Katherine E. & Salloum, Alison, 2018. "Child welfare workers' sexism and beliefs about father involvement," Children and Youth Services Review, Elsevier, vol. 89(C), pages 132-144.
    11. Kim, Hyun Ju & Chung, Jae Young, 2020. "Factors affecting youth citizenship in accordance with socioeconomic background," Children and Youth Services Review, Elsevier, vol. 111(C).
    12. Qianru Liang & Jimmy de la Torre & Nancy Law, 2023. "Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 690-718, December.
    13. Tobias A. Bauer & Alexandro Folster & Tina Braun & Timo von Oertzen, 2021. "A Group Comparison Test under Uncertain Group Membership," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 920-937, December.
    14. Zhao, Chunkai & Li, Xing, 2022. "Living under the shadow: Adverse childhood experiences and entrepreneurial behaviors in Chinese adults," Journal of Business Research, Elsevier, vol. 138(C), pages 239-255.
    15. Bartolucci, Francesco & Montanari, Giorgio E. & Pandolfi, Silvia, 2015. "Three-step estimation of latent Markov models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 287-301.
    16. Durante, Daniele & Canale, Antonio & Rigon, Tommaso, 2019. "A nested expectation–maximization algorithm for latent class models with covariates," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 97-103.
    17. Margot Bennink & Marcel A. Croon & Brigitte Kroon & Jeroen K. Vermunt, 2016. "Micro–macro multilevel latent class models with multiple discrete individual-level variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 139-154, June.
    18. Sarstedt, Marko & Radomir, Lăcrămioara & Moisescu, Ovidiu Ioan & Ringle, Christian M., 2022. "Latent class analysis in PLS-SEM: A review and recommendations for future applications," Journal of Business Research, Elsevier, vol. 138(C), pages 398-407.
    19. Zhu, Yajing & Steele, Fiona & Moustaki, Irini, 2020. "A multilevel structural equation model for the interrelationships between multiple latent dimensions of childhood socio‐economic circumstances, partnership transitions and mid‐life health," LSE Research Online Documents on Economics 103104, London School of Economics and Political Science, LSE Library.
    20. Mitchell George E., 2024. "Three Models of US State-Level Charity Regulation," Nonprofit Policy Forum, De Gruyter, vol. 15(1), pages 1-25, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:chinre:v:16:y:2023:i:4:d:10.1007_s12187-023-10036-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.