Caring Across Borders in the Digital Age: Emotional Labour, Burnout, and AI-Mediated Migrant Care Work in Europe’s Ageing Societies

Résumé

Abstract

Background Statement: Europe is navigating a complex intersection of an unprecedented demographic shift, characterized by rapid population ageing, and a structural deficit in formal long-term care (LTC) provision. To sustain their social care systems, wealthier European nations have become structurally dependent on cross-border migrant care labor, creating what sociological literature conceptualizes as "Global Care Chains." Concurrently, emerging digital solutions, ranging from Natural Language Processing (NLP) scribes to automated ambient sensors, are increasingly introduced to alleviate these structural challenges. This empirical paper investigates the intersection of transnational mobility, emotional labor, burnout, and Artificial Intelligence (AI) among migrant elderly care workers in Europe.

Methodology: Drawing on a qualitative phenomenological design involving semi-structured in-depth interviews with migrant caregivers (N = 24), this study explores how cross-border care workers navigate the dual burden of professional emotional regulation and transnational family caregiving within changing technological environments. Using Arlie Hochschild's Emotional Labour Theory, Christina Maslach's Burnout Triad, and Joan Tronto's Ethics of Care, thematic analysis reveals that migrant caregivers experience acute "emotional dissonance" driven by intensive surface acting, structural isolation, and systemic precarity.

Sample: The sample involves a qualitative phenomenological design involving semi-structured in-depth interviews with migrant caregivers (N = 24). The final sample consisted of 24 female migrant care workers currently or recently employed in Western European elderly care settings (Germany, Austria, and Italy). Country of Origin: Hungary (9, 37.5%), Romania (8, 33.3%), Poland (7, 29.2%). Employment Model: 24-Hour Live-in (Circular) (14, 58.3%), Formal Institutional Care (10, 41.7%).

Conclusion: While AI tools offer a critical structural buffer by automating administrative tasks and bridging linguistic gaps, their deployment introduces new socio-technical stressors, including automated surveillance and the erosion of private, off-duty spaces. The paper concludes with actionable policy recommendations aimed at aligning the European Union's Artificial Intelligence Act with workforce welfare, regularizing informal care arrangements, and establishing sustainable, ethically grounded socio-technical care economies.

Keywords: Migrant Care Work, Emotional Labour, Burnout, Global Care Chains, Artificial Intelligence, Socio-Technical Care, Ageing Europe