Publication date: Oct 06, 2024
Despite increasing attention on emotional labor in teacher well-being research, person-centered studies are relatively scarce, particularly concerning the emotional labor of online teaching during COVID-19 and its effects on teachers’ non-work-related mental health. This study aims to address these gaps by examining emotional labor profiles and their consequences on job satisfaction, depression, and anxiety among Chinese teachers involved in either online or offline teaching during October-December 2022. Two samples of teachers were analyzed altogether: one engaged in online teaching (N=605) and the other in offline teaching (N=394). Latent profile analysis was used to identify emotional labor profiles based on three strategies: surface acting, deep acting, and expression of naturally felt emotions. A total of four subgroups of emotional workers were identified: natural expressors, actors, flexible regulators, and authentic regulators. Significant differences were found between online and offline teaching, with a higher proportion of actors and fewer flexible regulators in the online condition, suggesting that the screen acts as a barrier to authentic emotional display. Among the four classes, actors scored lowest on job satisfaction and highest on depression and anxiety, whereas authentic regulators were the most adaptive, especially in online settings. The findings highlight the impact of online teaching on teachers’ emotional labor profiles and mental health, with practical implications for optimizing online teaching environments and supporting teacher well-being.
Concepts | Keywords |
---|---|
Chinese | emotional labour |
Covid | latent profile analysis |
Offline | mental health |
Teachers | online teaching |
Semantics
Type | Source | Name |
---|---|---|
disease | MESH | COVID-19 |
disease | MESH | depression |
disease | MESH | anxiety |