Year: 2024
Type: article
Abstract: The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings are not universally applicable. Using neural network feature extraction technology, this study aims to investigate the factors contributing to the high depression risk of doctoral students and effectively identify doctoral students at depression risk, so as to propose corresponding improvement strategies to prevent and intervene doctoral students with depression risk for universities. Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. Subsequently, we trained the optimal prediction model to predict the doctoral students with depression risk using a Multilayer Perceptron (MLP), achieving an accuracy of 89.09% on the test set. Additionally, this study constructed a group portrait of doctoral students at risk of depression, and found that overwork, poor work-life balance, and poor supervisor-student relationship, etc., were typical characteristics among these students. Finally, we proposed several improvement strategies for higher education institutions. Our research offers a new perspective on utilising artificial intelligence (AI) methods to tackle educational challenges, particularly in the identification and support of doctoral students at risk of depression, thereby enhancing their mental health.
Source: Scientific Reports
Authors: Changhong Teng, Chunmei Yang, Qiushi Liu
Institutions: Beijing Institute of Technology, MediaTek (China)
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,590
Funder: Beijing Institute of Technology
Award: G470298429
OpenAlex ID: W4405897657