Application of the dual-factor model of mental health among Chinese new generation of migrant workers

BMC Psychol. 2021 Nov 30;9(1):188. doi: 10.1186/s40359-021-00693-5.

Abstract

Background: Traditional models of mental health focus on psychopathological symptoms. In contrast, the dual-factor model of mental health integrates positive mental health and psychopathology into a mental health continuum, which is an adaptation and complement to the traditional mental health research paradigm. The new generation of migrant workers is an important part of current Chinese society. Their identity has created a sense of loneliness, rootlessness, and alienation. This paper validates the applicability of the dual-factor model of mental health among new generation migrant workers in China.

Methods: In this study, 600 new generation migrant workers were recruited and tested on the symptom checklist 90, satisfaction with life scale, perceived stress scale, employee engagement inventory. Descriptive statistics and ANOVA were performed, the differences between the unidimensional model and dual-factor model were also tested.

Results: The results showed that the dual-factor model of the mental health approach had better construct validity than the unidimensional model. And four subgroups could be significantly discriminated by the dual-factor model: mentally healthy (58.45%), vulnerable (30.87%), symptomatic but content (3.11%), and troubled (7.57%). Compared to the other three groups, workers who were mentally healthy showed higher perceived work values and lower perceived work stress.

Conclusions: The study suggests that a dual-factor model of mental health can be applied to new generation migrant workers in China, with positive mental health and psychopathology being important predictors of mental health.

Keywords: Dual-factor model; New generation migrant workers; Positive mental health; Psychopathology.

MeSH terms

  • China
  • Health Status
  • Humans
  • Mental Health*
  • Socioeconomic Factors
  • Transients and Migrants*