Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross-Sectional Study

• Published in: Journal of nursing management 2023-05, Vol.2023, p.1-11
• Main Authors: Li, Shu, Sznajder, Kristin K., Ning, Lingfang, Gao, Hong, Xie, Xinyue, Liu, Shuo, Shao, Chunyu, Li, Xinru, Yang, Xiaoshi
• Format: Article
• Language: English
• Subjects: Algorithms; Burnout; Careers; Chi-square test; Chronic fatigue syndrome; Chronic illnesses; Coping; Coronaviruses; COVID-19; Cross-sectional studies; Demography; Epidemics; Fatigue; Hospitals; Machine learning; Mental depression; Mental disorders; Mental health; Nurses; Nursing; Nursing administration; Pandemics; Physical symptoms; Professionals; Psychosis; Quality of life; Questionnaires; Redundancy; Regression analysis; Response rates; Sleep disorders; Stress; Subsets; Tests; Variables
• ISSN: 0966-0429; 1365-2834
• DOI: 10.1155/2023/5524561

Background. Nurses’ high workload can result in depressive symptoms. However, the research has underexplored the internal and external variables, such as organisational support, career identity, and burnout, which may predict depressive symptoms among Chinese nurses via machine learning (ML). Aim. To predict nurses’ depressive symptoms and identify the relevant factors by machine learning (ML) algorithms. Methods. A self-administered smartphone questionnaire was delivered to nurses to evaluate their depressive symptoms; 1,431 questionnaires and 28 internal and external features were collected. In the training set, the use of maximum relevance minimum redundancy ranked the features’ importance. Five ML algorithms were used to establish models to identify nurses’ depressive symptoms using different feature subsets, and the area under the curve (AUC) determined the optimal feature subset. Demographic characteristics were added to the optimal feature subset to establish the combined models. Each model’s performance was evaluated using the test set. Results. The prevalence rate of depressive symptoms among Chinese nurses was 31.86%. The optimal feature subset comprised of sleep disturbance, chronic fatigue, physical fatigue, exhaustion, and perceived organisation support. The five models based on the optimal feature subset had good prediction performance on the test set (AUC: 0.871–0.895 and accuracy: 0.798–0.815). After adding the significant demographic characteristics, the performance of the five combined models slightly improved; the AUC and accuracy increased to 0.904 and 0.826 on the test set, respectively. The logistic regression analysis results showed the best and most stable performance while the univariate analysis results showed that external and internal personal features (AUC: 0.739–0.841) were more effective than demographic characteristics (AUC: 0.572–0.588) for predicting nurses’ depressive symptoms. Conclusions. ML could effectively predict nurses’ depressive symptoms. Interventions to manage physical fatigue, sleep disorders, burnout, and organisational support may prevent depressive symptoms.