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Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach.

Publication ,  Journal Article
Rothenberg, WA; Bizzego, A; Esposito, G; Lansford, JE; Al-Hassan, SM; Bacchini, D; Bornstein, MH; Chang, L; Deater-Deckard, K; Di Giunta, L ...
Published in: Journal of youth and adolescence
August 2023

Adolescent mental health problems are rising rapidly around the world. To combat this rise, clinicians and policymakers need to know which risk factors matter most in predicting poor adolescent mental health. Theory-driven research has identified numerous risk factors that predict adolescent mental health problems but has difficulty distilling and replicating these findings. Data-driven machine learning methods can distill risk factors and replicate findings but have difficulty interpreting findings because these methods are atheoretical. This study demonstrates how data- and theory-driven methods can be integrated to identify the most important preadolescent risk factors in predicting adolescent mental health. Machine learning models examined which of 79 variables assessed at age 10 were the most important predictors of adolescent mental health at ages 13 and 17. These models were examined in a sample of 1176 families with adolescents from nine nations. Machine learning models accurately classified 78% of adolescents who were above-median in age 13 internalizing behavior, 77.3% who were above-median in age 13 externalizing behavior, 73.2% who were above-median in age 17 externalizing behavior, and 60.6% who were above-median in age 17 internalizing behavior. Age 10 measures of youth externalizing and internalizing behavior were the most important predictors of age 13 and 17 externalizing/internalizing behavior, followed by family context variables, parenting behaviors, individual child characteristics, and finally neighborhood and cultural variables. The combination of theoretical and machine-learning models strengthens both approaches and accurately predicts which adolescents demonstrate above average mental health difficulties in approximately 7 of 10 adolescents 3-7 years after the data used in machine learning models were collected.

Duke Scholars

Published In

Journal of youth and adolescence

DOI

EISSN

1573-6601

ISSN

0047-2891

Publication Date

August 2023

Volume

52

Issue

8

Start / End Page

1595 / 1619

Related Subject Headings

  • Risk Factors
  • Parenting
  • Outcome Assessment, Health Care
  • Mental Health
  • Humans
  • Developmental & Child Psychology
  • Child Behavior Disorders
  • Child
  • Adolescent Behavior
  • Adolescent
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Rothenberg, W. A., Bizzego, A., Esposito, G., Lansford, J. E., Al-Hassan, S. M., Bacchini, D., … Alampay, L. P. (2023). Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach. Journal of Youth and Adolescence, 52(8), 1595–1619. https://doi.org/10.1007/s10964-023-01767-w
Rothenberg, W Andrew, Andrea Bizzego, Gianluca Esposito, Jennifer E. Lansford, Suha M. Al-Hassan, Dario Bacchini, Marc H. Bornstein, et al. “Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach.Journal of Youth and Adolescence 52, no. 8 (August 2023): 1595–1619. https://doi.org/10.1007/s10964-023-01767-w.
Rothenberg WA, Bizzego A, Esposito G, Lansford JE, Al-Hassan SM, Bacchini D, et al. Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach. Journal of youth and adolescence. 2023 Aug;52(8):1595–619.
Rothenberg, W. Andrew, et al. “Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach.Journal of Youth and Adolescence, vol. 52, no. 8, Aug. 2023, pp. 1595–619. Epmc, doi:10.1007/s10964-023-01767-w.
Rothenberg WA, Bizzego A, Esposito G, Lansford JE, Al-Hassan SM, Bacchini D, Bornstein MH, Chang L, Deater-Deckard K, Di Giunta L, Dodge KA, Gurdal S, Liu Q, Long Q, Oburu P, Pastorelli C, Skinner AT, Sorbring E, Tapanya S, Steinberg L, Tirado LMU, Yotanyamaneewong S, Alampay LP. Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach. Journal of youth and adolescence. 2023 Aug;52(8):1595–1619.
Journal cover image

Published In

Journal of youth and adolescence

DOI

EISSN

1573-6601

ISSN

0047-2891

Publication Date

August 2023

Volume

52

Issue

8

Start / End Page

1595 / 1619

Related Subject Headings

  • Risk Factors
  • Parenting
  • Outcome Assessment, Health Care
  • Mental Health
  • Humans
  • Developmental & Child Psychology
  • Child Behavior Disorders
  • Child
  • Adolescent Behavior
  • Adolescent