Predicting adolescent gaming addiction: insights from individual, family, and school-level factors via machine learning

Zirong Li, Ruyi Ding, Tuo Liu, Zhonghuang Su, Chun Shen

Open source

DOI: 10.1017/s0033291726106205
Published: 2026
Container: Psychological Medicine
Publisher: Cambridge University Press (CUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1017/s0033291726106205,
  title = {Predicting adolescent gaming addiction: insights from individual, family, and school-level factors via machine learning},
  author = {Zirong Li and Ruyi Ding and Tuo Liu and Zhonghuang Su and Chun Shen},
  year = {2026},
  journal = {Psychological Medicine},
  doi = {10.1017/s0033291726106205},
  url = {https://doi.org/10.1017/s0033291726106205}
}

RIS

TY  - JOUR
TI  - Predicting adolescent gaming addiction: insights from individual, family, and school-level factors via machine learning
AU  - Zirong Li
AU  - Ruyi Ding
AU  - Tuo Liu
AU  - Zhonghuang Su
AU  - Chun Shen
PY  - 2026
JO  - Psychological Medicine
DO  - 10.1017/s0033291726106205
UR  - https://doi.org/10.1017/s0033291726106205
ER  -

APA

Li, Z., Ding, R., Liu, T., Su, Z., & Shen, C. (2026). Predicting adolescent gaming addiction: insights from individual, family, and school-level factors via machine learning. Psychological Medicine. https://doi.org/10.1017/s0033291726106205

Source records

crossref · retrieved 2026-11-06T21:17:14.060Z