A single-center assessment of mental health and well-being in a biomedical sciences graduate program

Jachim, Sarah K.; Bowles, Bradley S.; Panicker, Anjali J; Yousaf, Iris; Brown, Alyssa; Zoroufy, Alexander J.; Boehmer, Kasey R.; Stonnington, Cynthia M.; Vadeboncoeur, Tyler; Lujan, James; Ehlers, Shawna L.; Schulze, Autumn J

doi:10.1038/s41587-023-01778-9

Cited by 2 publications

(1 citation statement)

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“…Consequently, this phenomenon introduces substantial deviations in the measurement outcomes of psychopathological indicators. Second, in the realm of psychological research, it has been demonstrated by previous studies [7][8][9][10][11] that demographic information, health-related data, and personal, contextual information encompassing significant life transitions during adolescence serve as crucial indicators for assessing the mental health of middle-aged and older adults. However, existing investigations predominantly focus on establishing associations between mental health and individual factors, lacking a comprehensive evaluation of multiple variables.…”

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confidence: 99%

Prediction Model of mental health of Middle-aged and older adults in China

Kong

2023

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The phenomenon of aging has emerged as a prominent concern within the context of China's economic and social progress. The middle-aged and elderly population suffers the dual burden of psychological and physiological challenges due to the decline in their physiological functions. However, the intricate nature of mental health diagnostic analysis poses difficulties in early predicting and intervening in mental health issues affecting middle-aged and elderly individuals. We attempt to establish a benchmark for evaluating the mental health of middle-aged and older adults based on psychological theories. Additionally, we propose a hypergraph-based mental health prediction model, termed MH-HGNN, specifically designed for this demographic. MH-HGNN incorporates a hypergraph structure to capture and process complex high-order correlation relationships by representing middle-aged and elderly individuals as nodes. By applying Laplace polynomial spectral convolution representation learning on these relationships, the model forecasts the mental health status of this population segment. Empirical findings indicate that MH-HGNN achieves an 82.7% accuracy rate in predicting mental health outcomes, surpassing the performance of prevalent deep learning baseline techniques like GNN, GAT, and GraphSAGE. Notably, the MH-HGNN model exhibits an improvement of up to 9.17% in accuracy compared to the baseline model.

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confidence: 99%