Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach.
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...
| Publicado en: | Journal of Youth & Adolescence Vol. 52; no. 8; pp. 1595 - 1620 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Aug2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=164370789&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 164370789 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00472891 JYA jtl: Journal of Youth & Adolescence issn: 00472891 maglogo: N pubinfo: dt: Aug2023 vid: 52 iid: 8 pid: 237 pub: Springer Nature artinfo: ui: 164370789 10.1007/s10964-023-01767-w ppf: 1595 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.5MB tig: atl: Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach. aug: au: Rothenberg, W. Andrew Bizzego, Andrea Esposito, Gianluca Lansford, Jennifer E. Al-Hassan, Suha M. Bacchini, Dario Bornstein, Marc H. Chang, Lei Deater-Deckard, Kirby Di Giunta, Laura Dodge, Kenneth A. Gurdal, Sevtap Liu, Qin Long, Qian Oburu, Paul Pastorelli, Concetta Skinner, Ann T. Sorbring, Emma Tapanya, Sombat Steinberg, Laurence affil: Duke University, Durham, NC, USA University of Miami, Coral Gables, FL, USA University of Trento, Trento, Italy Hashemite University, Zarqa, Jordan University of Naples "Federico II", Naples, Italy Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland, USA UNICEF, New York, New York, USA University of Macau, Zhuhai, China University of Massachusetts, Amherst, MA, USA Università di Roma "La Sapienza", Rome, Italy University West, Trollhättan, Sweden Chongqing Medical University, Chongqing, China Duke Kunshan University, Suzhou, China Maseno University, Maseno, Kenya Chiang Mai University, Chiang Mai, Thailand Temple University, Philadelphia, PA, USA King Abdulaziz University, Jeddah, Saudi Arabia su: Mental illness risk factors Culture Adolescent health Teenagers' conduct of life Adolescence Machine learning Risk assessment Research funding Descriptive statistics Prediction models sug: subj: Mental illness risk factors Culture Adolescent health Teenagers' conduct of life Adolescence Machine learning Risk assessment Research funding Descriptive statistics Prediction models keyword: Externalizing Internalizing Parenting Prediction Externalizing Internalizing Parenting Prediction ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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