Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic

The study utilized machine learning to identify predictors of hair cortisol concentration in mothers and infants before and during the COVID-19 pandemic. Hair cortisol concentration is a biomarker of chronic stress, and understanding its predictors can help in assessing stress levels. The research aimed to explore how the pandemic affected stress indicators in both mothers and infants, providing insights into the psychological and physiological impacts of the pandemic. The study's findings could contribute to developing strategies for managing stress in similar future events. However, specific details about the number of participants or the exact predictors identified were not provided in the summary.