Using machine learning to identify the strongest personal, school, and family correlates of youth mental health and well-being.

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Publication Details

Journal
Journal of Psychopathology and Clinical Science
Published
2026-02-26
DOI
https://doi.org/10.1037/abn0001103
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
0.00
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article

Using machine learning to identify the strongest personal, school, and family correlates of youth mental health and well-being.

Katholiki Georgiades, Hause Lin, Lydia M Li, Mark Wade
Journal of Psychopathology and Clinical Science
Mental Health Research Topics
article

Using machine learning to identify the strongest personal, school, and family correlates of youth mental health and well-being.

Katholiki Georgiades, Hause Lin, Lydia M Li, Mark Wade
article en

Abstract

No abstract available for this paper.
Journal of Psychopathology and Clinical Science
University of Toronto (CA), Massachusetts Institute of Technology (US), McMaster University (CA)
Quality Education
Openalex Percentile: Top 45%
Mental Health Research Topics
DOISourceOpenAlex
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