Evidence map›Paper›PMID 41869605›Full record

ArticleFrontiers in public health2026

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.

Shang Gao, Qiyuan Wang, Keyao Kang, Yuxin Chen, Xiangchen Kong, Xihe Yu

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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shang GaoSchool of Public Health, Jilin University, Changchun, Jilin, China.
Qiyuan WangSchool of Public Health, Jilin University, Changchun, Jilin, China.
Keyao KangSchool of Public Health, Jilin University, Changchun, Jilin, China.
Yuxin ChenSchool of Public Health, Jilin University, Changchun, Jilin, China.
Xiangchen KongSchool of Public Health, Jilin University, Changchun, Jilin, China.
Xihe YuSchool of Public Health, Jilin University, Changchun, Jilin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

IndustryJob SatisfactionMental HealthAdultAnxietyChinaCross-Sectional StudiesDepressionFrontline WorkersHumansJob SecurityMaleMiddle AgedSurveys and Questionnairesanxietydepressiondigital-intelligence job insecurityindustrial workersjob satisfaction

Identifiers

PMID41869605
PMCPMC13002619

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.