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The mental health and well-being of students and teachers during the COVID-19 pandemic: Combining classical statistics and machine learning approaches
Norman Biliwang MENDOZA
, Ronnel B. KING
, Joseph Y. HAW
Department of Curriculum and Instruction (C&I)
Research output
:
Contribution to journal
›
Articles
›
peer-review
16
Citations (Scopus)
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Dive into the research topics of 'The mental health and well-being of students and teachers during the COVID-19 pandemic: Combining classical statistics and machine learning approaches'. Together they form a unique fingerprint.
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Keyphrases
Well-being
100%
Mental Health
100%
Strong Predictor
100%
Statistical Learning
100%
Machine Learning Approach
100%
Classical Statistics
100%
COVID-19 Pandemic
100%
Teacher Well-being
66%
Copyright
33%
Contextual Factors
33%
Philippines
33%
Family Support
33%
School Closure
33%
Mental Health Outcomes
33%
Well-being Outcomes
33%
Predictors of Anxiety
33%
Positive Well-being
33%
Coronavirus Disease (COVID)
33%
Fear of COVID
33%
Social Sciences
Wellbeing
100%
Mental Health
100%
Health and Well-Being
100%
COVID 19 Epidemic
100%
UK
20%
Psychology
20%
Contextual Factor
20%
Psychology
Mental Health
100%
Health and Well-Being
100%