The Mental Health and Well-Being of Students and Teachers during the COVID-19 Pandemic: Combining Classical Statistics and Machine Learning Approaches

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  • Author(s): Mendoza, Norman B. (ORCID Mendoza, Norman B. (ORCID 0000-0003-0344-0709); King, Ronnel B. (ORCID King, Ronnel B. (ORCID 0000-0003-1723-1748); Haw, Joseph Y. (ORCID Haw, Joseph Y. (ORCID 0000-0002-8759-7705)
  • Language:
    English
  • Source:
    Educational Psychology. 2023 43(5):430-451.
  • Publication Date:
    2023
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
    • Peer Reviewed:
      Y
    • Source:
      22
    • Education Level:
      Elementary Secondary Education
    • Subject Terms:
      Mental Health; Well Being; Predictor Variables; COVID-19; Pandemics; School Closing; Depression (Psychology); Anxiety; Student Characteristics; Teacher Characteristics; Family Influence; Stress Variables; Fear; Foreign Countries; Social Influences; Principals; Administrator Role; Artificial Intelligence; Elementary Secondary Education
    • Subject Terms:
      Philippines
    • Accession Number:
      10.1080/01443410.2023.2226846
    • ISSN:
      0144-3410
      1469-5820
    • Abstract:
      The aims of this study were to (1) to explore the state of students' and teachers' well-being and (2) examine the factors that predict their well-being during the pandemic-related school closures in the Philippines. Our sample comprised 733 students and 1168 teachers. During the height of the pandemic, 22.10% of the students and 13.44% of teachers met the cut-off for depression; 13.91% of the students and 15.92% of the teachers met the cut-off for anxiety. Both classical statistics and machine learning approaches were used to identify the roles of demographic, psychological, and socio-contextual factors that statistically predicted well-being outcomes. Results highlighted that family support was the strongest predictor of students' and teachers' positive well-being. For mental health outcomes, the strongest predictors of depression were anxiety and stress, while the strongest predictors of anxiety were depression, stress, and fear of COVID. Implications for students' and teachers' well-being amidst COVID are discussed.
    • Abstract:
      As Provided
    • Publication Date:
      2023
    • Accession Number:
      EJ1396483