eCite Digital Repository

Mental illness, social suffering and structural antagonism in the labour process


Woods, M and Macklin, R and Dawkins, S and Martin, Angela, Mental illness, social suffering and structural antagonism in the labour process, Work, Employment and Society, 33, (6) pp. 948-965. ISSN 0950-0170 (2019) [Refereed Article]


Copyright Statement

Copyright 2019 The Authors

DOI: doi:10.1177/0950017019866650


Workplace conditions and experiences powerfully influence mental health and individuals experiencing mental illness, including the extent to which people experiencing mental ill-health are ‘disabled’ by their work environments. This article explains how examination of the social suffering experienced in workplaces by people with mental illness could enhance understanding of the inter-relationships between mental health and workplace conditions, including experiences and characteristics of the overarching labour process. It examines how workplace perceptions and narratives around mental illness act as discursive resources to influence the social realities of people with mental ill-health. It applies Labour Process Theory to highlight how such discursive resources could be used by workers and employers to influence the power, agency and control in workplace environments and the labour process, and the implications such attempts might have for social suffering. It concludes with an agenda for future research exploring these issues.

Item Details

Item Type:Refereed Article
Keywords:employee mental health, labour process theory, mental illness, social suffering
Research Division:Commerce, Management, Tourism and Services
Research Group:Strategy, management and organisational behaviour
Research Field:Organisation and management theory
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in commerce, management, tourism and services
UTAS Author:Woods, M (Dr Megan Woods)
UTAS Author:Dawkins, S (Dr Sarah Dawkins)
UTAS Author:Martin, Angela (Professor Angela Martin)
ID Code:134487
Year Published:2019
Web of Science® Times Cited:9
Deposited By:Management
Deposited On:2019-08-14
Last Modified:2022-08-30
Downloads:29 View Download Statistics

Repository Staff Only: item control page