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. |
|---|---|
| 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.) |
| References: | Children (Basel). 2025 Aug 18;12(8):. (PMID: 40868534) J Child Psychol Psychiatry. 2026 Jan;67(1):55-66. (PMID: 40757405) J Sch Health. 2018 Feb;88(2):101-111. (PMID: 29333642) Nat Mach Intell. 2020 Jan;2(1):56-67. (PMID: 32607472) Appl Psychol Health Well Being. 2026 Oct;18(5):e70211. (PMID: 42698279) Appl Psychol Health Well Being. 2025 Feb 25;17(1):e12624. (PMID: 39523935) J Child Psychol Psychiatry. 2011 Oct;52(10):1052-62. (PMID: 21834918) Scand J Psychol. 2024 Oct;65(5):858-869. (PMID: 38785185) Bioinformatics. 2012 Jan 1;28(1):112-8. (PMID: 22039212) Int J Environ Res Public Health. 2022 Jun 10;19(12):. (PMID: 35742383) Front Psychol. 2024 Jul 02;15:1407338. (PMID: 39015327) J Sch Health. 2017 Jan;87(1):71-80. (PMID: 27917486) Adv Child Dev Behav. 2011;41:197-230. (PMID: 23259193) J Educ Psychol. 2015 Feb 1;107(1):309-320. (PMID: 25937669) J Youth Adolesc. 2024 Aug;53(8):1903-1917. (PMID: 38622470) Depress Anxiety. 2003;18(2):76-82. (PMID: 12964174) BMC Nurs. 2021 Jul 5;20(1):119. (PMID: 34225712) Int J Adolesc Med Health. 2013;25(4):335-44. (PMID: 23612532) Int J Environ Res Public Health. 2022 Jan 18;19(3):. (PMID: 35162088) BMC Psychol. 2025 Dec 9;14(1):45. (PMID: 41366508) J Adolesc. 2025 Jan;97(1):180-195. (PMID: 39315619) Appl Psychol Health Well Being. 2025 Feb;17(1):e12635. (PMID: 39668656) Clin Transl Sci. 2024 Nov;17(11):e70056. (PMID: 39463176) Soc Sci Med. 2015 May;132:261-9. (PMID: 25176335) Health Psychol. 2000 Nov;19(6):586-92. (PMID: 11129362) J Adolesc Health. 2021 Apr;68(4):676-682. (PMID: 33583684) Nat Ment Health. 2024;2(10):1217-1230. (PMID: 39464304) Lancet Psychiatry. 2025 Oct;12(10):723. (PMID: 40967722) Child Dev. 2012 Jan-Feb;83(1):120-36. (PMID: 22181046) Annu Rev Clin Psychol. 2021 May 7;17:521-549. (PMID: 33534615) J Youth Adolesc. 1987 Dec;16(6):561-77. (PMID: 24277491) |
| 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 |
| ISSN: | 1758-0854 |
|---|---|
| DOI: | 10.1111/aphw.70211 |