Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data.

Zelin Liu, Zékai Lu, Yaqiong Wang, Danhua Lin

Abstract read

In one paragraph

Article in Applied psychology. Health and well-being, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data.Applied psychology. Health and well-being · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zelin LiuInstitute of Developmental Psychology, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0002-2808-2250

Zékai LuInstitute of Developmental Psychology, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0001-8015-3071

Yaqiong WangDepartment of Psychology, Hangzhou Normal University, Hangzhou, China.ORCID https://orcid.org/0000-0002-0330-3432

Danhua LinInstitute of Developmental Psychology, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0003-3755-1730

Funding

National Natural Science Foundation of China 32471116

6 · The paper itself

Abstract

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 (

Indexed as

Adolescent DevelopmentBoosting Machine Learning AlgorithmsDepressionLonelinessPrediction AlgorithmsAdolescentChildChinaEast Asian PeopleFemaleHumansLongitudinal StudiesMaleChinese adolescentsheterogeneity analysislongitudinal predictionmachine learningpositive youth developmentSHAP

Identifiers

PMID42698279

DOI10.1111/aphw.70211

PMCPMC13545513

What OpenQuestion holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.