Platform Labor Control, Hidden Exploitation and Labor Alienation of Accompanying Workers

With the intensification of aging in mainland China, an increasing number of elderly people need assistance in seeking medical treatment. Additionally, the uneven spatial distribution of medical resources has led to a rise in demand for escort services for cross-regional medical visits. Driven by these factors, the demand for accompanying services has been growing rapidly. , with more workers joining the industry and numerous accompanying platform enterprises emerging. However, since this occupation has not yet been included in the national occupational qualification directory, and the medical assistance industry possesses unique characteristics, peizhenshi (accompanying workers) relying on platforms to take orders face novel labor dilemmas, particularly regarding platform labor exploitation and labor control—phenomena that existing research has rarely addressed. This study, based on in-depth interviews with 15 accompanying workers and related personnel, analyzes how platforms reshape the labor process of accompanying workers. It illustrates that platforms exert control through three strategies: contextual pre-assessment, where fragmented medical information disclosure creates a cognitive “black box” requiring workers’ autonomous interpretation during order allocation; skill combination, which ties professional medical assistance to emotional labor as a “compound skill package” while enforcing flat hourly wages to enable invisible exploitation; and moral pressure, where service ratings are moralized into indicators of professional ethics to coerce workers into perpetual ethical performance, alienating labor value. Specifically, in the order allocation phase, platforms use fragmented medical information disclosure to create information gaps that workers must fill themselves; in the service delivery phase, platforms attempt to control the labor process through standardized procedures, but due to the special nature of medical scenarios, must implement semi-standardized or non-standardized labor control, during which accompanying workers need to combine professional medical assistance skills, emotional labor, and other skills to address different service scenarios—however, although accompanying workers likely utilize multiple skills, their compensation is often calculated solely based on time or other single standards, constituting invisible exploitation; finally, in the evaluation phase, by transforming service ratings into indicators of professional ethics, (emotional) labor value is alienated into accompanying workers' continuous moral performance. These mechanisms shift platform responsibilities onto workers, resulting in three dilemmas: (i) workers bear medical decision-making risks due to information asymmetry; (ii) skill-reward mismatches fuel occupational burnout; and (iii) moralized evaluations incentivize excessive emotional labor. To address these issues, three recommendations are proposed: establishing an emotional labor valuation system to monetize intangible services like crisis intervention; platforms should disclose medical workflow data and assume training obligations; and fostering professional collectives for collective bargaining to resist flexible control. These measures aim to rebuild digital labor ethics, protect the legitimate rights and interests of accompanying workers, and improve the quality of accompanying services.