Psychological Distress Among Chinese Manufacturing Employees: Prevalence and a Symptom Network Analysis

Abstract

The psychological distress among manufacturing workers is an increasingly important issue and has attracted extensive attention. However, the mental health of this subgroup of the Chinese population is underexplored. This study aimed to evaluate the prevalence of psychological distress in Chinese manufacturing employees and identify central symptoms, important bridge symptoms, and associations between symptoms using network analysis. The participants were 4934 employees recruited from a Chinese manufacturing company. The Maslach Burnout Inventory-General Survey (MBI-GS), the Self-Rating Anxiety Scale (SAS), the Self-Rating Depression Scale (SDS), and the Symptom Checklist 90 (SCL-90) were used to assess job burnout, anxiety, depression, compulsive symptom, somatization, psychoticism, paranoid, phobic, hostility, and interpersonal sensitivity, respectively. In total, 29.77%, 21.14%, and 26.53% of all participants experienced burnout, anxiety, and depression, respectively. Compared to normative data of the Chinese population, the seven symptoms of the SCL-90 among participants were significantly higher. The network analysis revealed that interpersonal sensitivity had the greatest strength and somatization had the greatest betweenness and closeness. Anxiety had the highest bridge expected influence. These results demonstrate that the mental health of Chinese manufacturing employees is a cause for concern. Interpersonal sensitivity and somatization emerged as the core symptoms, and anxiety was an important bridge symptom. Interventions aimed at these conditions may promote and enhance the overall mental health of Chinese manufacturing employees.

Keywords: anxiety; burnout; depression; manufacturing employees; network analysis.

FIGURE 1

Partial correlation network of exhaustion, cynicism, reduced personal efficacy, depression, anxiety, and SCL‐90 subscales. Blue lines were used to represent positive edges, and red lines were used to represent negative edges. The thicker and more saturated the line, the stronger the connection. Nodes identified as bridge symptoms are colored in blue. Interpersonal sensitivity had the greatest strength, and somatization had the greatest betweenness and closeness. Anxiety had the highest bridge expected influence.

FIGURE 2

Case dropping bootstrap for the network shown in Figure 1. The dots represent the average correlation between centrality indices of the original network and the bootstrapped networks consisting of a subset of participants (with decreasing sample size from left to right).