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Dose Higher Socioeconomic Status Associate With Better Mental Health Outcomes Among Primary Caregivers In Rural China?, Wenwen Zhang

Master's Theses

This study explores the intricate relationship between socioeconomic status (SES) and the mental health outcomes of primary caregivers in rural China, focusing on the Early Childhood Development (ECD) context in Ya'an. Anchored in a major intervention project launched in 2023, the study critically examines the hypothesis that higher socioeconomic status correlates with lower levels of negative emotions among caregivers.

Utilizing cross-sectional data, the research investigates how educational attainment and household income influence caregivers' mental health, measured through the DASS-21 score in three dimensions: stress, anxiety, and depression. The findings reveal that higher household income and educational levels are significantly associated …

Using Machine Learning To Analyze Children’S Drawings As Indicators Of Mental Well-Being, Nicholas R. Lawrie

Master's Theses

Human figure drawings are a well-studied diagnostic tool for emotional distress in children. Cleft lip/palate is one of the most common birth defects in the world, and has been shown to negatively impact emotional well-being in childhood which can have negative economic consequences in adulthood. Utilizing a dataset of human figure drawings from children in India and survey data on mental health, this paper will assess the impact of corrective surgery on mental health outcomes, as well as assess the validity of the drawing emotional indicators themselves. The results indicate that while the emotional indicators may not be valid in …

Estimating Predictors Of Mental Well-Being Through Analysis Of Children’S Drawings: The Case Of Syrian Refugees, Stephanie Smith

Master's Theses

There are currently over 65 million individuals that have been forcibly displaced globally. The cumulative trauma that comes from the refugee experience and exposure to violence has proven to have long-term negative psychological outcomes and thus negative impacts on human capital in the long run. Given that over 50% percent of the global refugee population are children, the ability to efficiently and accurately assess their mental well-being is of critical importance. Using data from over 2000 refugee children in Jordan, I use machine learning techniques to find key predictors of psychological distress, PTSD, and exposure to violence found in children’s …