Profiles of job satisfaction among industrial workers and its association with mental health under the background of Industry 5.0 transformation

A latent profile analysis

Bibliographic Data

ID22090752
AuthorsShang Gao (0000-0001-8641-2433, Jilin University), Qiyuan Wang (0000-0003-2529-6226, Jilin University), Keyao Kang (Jilin University), Yuxin Chen (0009-0004-6241-6535, Jilin University), Y Q Chen (0009-0002-7350-7091, Jilin University), Xiangchen Kong (Jilin University), Xiaoyan Yu (0000-0003-3861-7340, Jilin University, corresponding author), Xihe Yu (0000-0002-7076-1062, Jilin University)
Year2026
Volume14
Pages1772767-1772767
Publication date2026-03-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1772767
PMID41869605
OpenAlexW7134078973
LanguageEN
References cited99

Background: Job satisfaction is a critical factor influencing workplace efficiency and employee well-being. In the context of Industry 5.0 transformation, understanding the latent profiles of job satisfaction and their relationship with mental health outcomes, such as depression, anxiety, and digital-intelligence job insecurity, is critical for promoting employee well-being and organizational sustainability. This study aims to explore the latent profiles of job satisfaction among industrial workers and explore their associations with mental health outcomes. Methods: This study used cross-sectional data from 3,420 male frontline workers from a large automobile manufacturing enterprise in Jilin Province, China in April 2024. Latent profile analysis (LPA) was employed to identify distinct latent profiles of job satisfaction among industrial workers, while hierarchical linear regression analysis was used to analyze the relationship between job satisfaction and psychological health outcomes (depression, anxiety and digital-intelligence job insecurity). Results: The score of job satisfaction among industrial workers in Jilin Province was 3.62 ± 0.90. Four profiles were identified: very low (5.97%), low-to-moderate (31.14%), moderately high (42.63%), and high job satisfaction (20.26%). Depression and anxiety showed a clear level-gradient pattern across profiles, whereas digital-intelligence job insecurity displayed a non-monotonic pattern with higher levels in the low-to-moderate and moderately high profiles. Work stress showed consistent associations with all outcomes, and job satisfaction profiles remained associated with depression and anxiety after covariate and stress adjustment; associations with digital-intelligence job insecurity were smaller but detectable. Conclusion: This study examined heterogeneity in job satisfaction among frontline industrial workers and its associations with mental health outcomes. Latent profile analysis identified four job satisfaction profiles. Job satisfaction profile membership remained strongly associated with depression and anxiety. Digital-intelligence job insecurity showed a non-monotonic pattern across profiles. These findings suggest that an individual-centered profile approach provides actionable differentiation of mental health symptom burden across distinct job satisfaction patterns, supporting more targeted workplace strategies

Anxiety · Job attitude · Job satisfaction · Latent class model · Mental health · Multilevel model · COVID-19 and Mental Health · Technostress in Professional Settings · Workplace Health and Well-being

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