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<p>While the gig economy has garnered significant attention, the systematic assessment and targeted prevention of mental health among gig workers remain underexplored. This study adapted a dual-factor model to examine the mental health of gig economy workers (<i>N</i> = 1,093) and its predictors. Specifically, we used latent profile analysis (LPA) to identify mental health profiles based on three key indicators: employee well-being, occupational belonging, and the General Health Questionnaire-12 (GHQ-12). We explored the associations between these profiles and individual, microsystem, and macrosystem factors. LPA identified four latent profiles of dual-factor mental health, namely, <i>complete mental health</i> (20.8%), <i>troubled</i> (6.9%), <i>symptomatic but content</i> (34.7%), and <i>vulnerable</i> (37.6%). Factors such as work values, working duration, working intensity, and medical conditions were significantly related to these profiles. These findings underscore the prominent classification characteristics of dual-factor mental health among gig economy workers, providing an empirical foundation for the development of targeted prevention and intervention strategies.</p>

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Latent profile analysis of mental health for gig economy workers: a dual-factor model perspective

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Ying Huang,
  • Ting Hu,
  • Yan Lin,
  • Qiaohua Yu,
  • Rongmao Lin

摘要

While the gig economy has garnered significant attention, the systematic assessment and targeted prevention of mental health among gig workers remain underexplored. This study adapted a dual-factor model to examine the mental health of gig economy workers (N = 1,093) and its predictors. Specifically, we used latent profile analysis (LPA) to identify mental health profiles based on three key indicators: employee well-being, occupational belonging, and the General Health Questionnaire-12 (GHQ-12). We explored the associations between these profiles and individual, microsystem, and macrosystem factors. LPA identified four latent profiles of dual-factor mental health, namely, complete mental health (20.8%), troubled (6.9%), symptomatic but content (34.7%), and vulnerable (37.6%). Factors such as work values, working duration, working intensity, and medical conditions were significantly related to these profiles. These findings underscore the prominent classification characteristics of dual-factor mental health among gig economy workers, providing an empirical foundation for the development of targeted prevention and intervention strategies.