Academic Journal
Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data.
| Τίτλος: | Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data. |
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| Συγγραφείς: | Liu Z; Institute of Developmental Psychology, Beijing Normal University, Beijing, China., Lu Z; Institute of Developmental Psychology, Beijing Normal University, Beijing, China.; Department of Sociology, McGill University, Montréal, Canada., Wang Y; Department of Psychology, Hangzhou Normal University, Hangzhou, China., Lin D; Institute of Developmental Psychology, Beijing Normal University, Beijing, China. |
| Πηγή: | Applied psychology. Health and well-being [Appl Psychol Health Well Being] 2026 Oct; Vol. 18 (5), pp. e70211. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Blackwell Country of Publication: England NLM ID: 101502957 Publication Model: Print Cited Medium: Internet ISSN: 1758-0854 (Electronic) Linking ISSN: 17580854 NLM ISO Abbreviation: Appl Psychol Health Well Being Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford : Blackwell |
| Ιατρικοί όροι (MeSH): | Depression*/psychology , Loneliness*/psychology , Adolescent Development* , Boosting Machine Learning Algorithms* , Prediction Algorithms*, Adolescent ; Child ; Female ; Humans ; Male ; China ; Longitudinal Studies ; East Asian People |
| Περίληψη: | Despite growing recognition that positive youth development (PYD) depends on the dynamic interaction of individual and ecological resources, existing studies rely on linear models that cannot capture high-dimensional, nonlinear predictor configurations. This study applied machine learning to four-wave longitudinal data from 5019 Chinese adolescents (ages 9-19) to identify the key predictors of PYD at T4 (controlling for prior PYD at T3), measured by the Chinese 4Cs model (Character, Competence, Confidence, Connection). We compared 12 algorithms; CatBoost achieved the best prediction ( INLINEMATH = .816). SHAP analysis identified school psychological climate, depression, and parental loneliness as the top three predictors. Heterogeneity analyses revealed an age gradient: School climate dominated for primary and middle school students, whereas parental loneliness dominated for high school students. Student type analyses uncovered three distinct developmental pathways: an aspirational pathway characterized by social mobility belief for migrant children, a relational pathway characterized by parental loneliness for left-behind and urban children, and a clinical pathway characterized by depression for rural ordinary children. These findings provide empirical support for differentiated, context-sensitive intervention strategies targeting PYD across diverse Chinese adolescent populations. (© 2026 International Association of Applied Psychology.) |
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| Grant Information: | 32471116 National Natural Science Foundation of China |
| Contributed Indexing: | Keywords: Chinese adolescents; SHAP; heterogeneity analysis; longitudinal prediction; machine learning; positive youth development |
| Entry Date(s): | Date Created: 20260905 Date Completed: 20260905 Latest Revision: 20260908 |
| Update Code: | 20260909 |
| PubMed Central ID: | PMC13545513 |
| DOI: | 10.1111/aphw.70211 |
| PMID: | 42698279 |
| Βάση Δεδομένων: | MEDLINE |
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