Clinical Report: Clustering Psychological Response Patterns Using Machine Learning
Overview
This study identifies three psychological response clusters among adults during the COVID-19 pandemic, highlighting the dynamic nature of mental health across two waves.
Background
The COVID-19 pandemic has led to widespread psychological distress. This study aims to examine how psychological clusters evolve across pandemic waves.
Data Highlights
Cluster
Wave 1 (%)
Wave 2 (%)
Resilient (low-distress, high-coping)
35.5
29.0
Low-coping
40.04
49.11
High-distress
25.06
21.89
Key Findings
Three clusters emerged: resilient, low-coping, and high-distress.
55.3% of participants remained in the same cluster across waves.
55.8% of those in the resilient cluster transitioned to higher-distress clusters by Wave 2.
The low-coping cluster exhibited the greatest stability at 68.9%.
Older age, higher education, and employment were associated with the resilient cluster.
Younger age and unemployment correlated with higher distress levels.
Clinical Implications
Understanding the dynamic nature of psychological responses during crises can inform targeted interventions. Strengthening coping skills and addressing socioeconomic factors may enhance mental health support strategies.
Conclusion
The study highlights the heterogeneous nature of psychological responses during the COVID-19 pandemic.
Systematic review of 8 observational studies found limited evidence on associations between prenatal asthma-medication exposure and neurodevelopmental outcomes, with autism spectrum disorder the only outcome suitable for meta-analysis.
