众包型在线心理健康服务质量的识别、影响机制与干预策略OACHSSCD

Crowdsourced online mental health services have met patients'diverse needs and improved service efficiency.However,the relatively low entry barriers for service providers have led to uneven service quality,which in turn constrains platform development.Existing literature rarely examines the dynamic evolution of service quality on such platforms from the perspective of service providers.This research aims to enhance service quality in crowdsourced online mental health services from three perspectives:identification,influence,and intervention.First,drawing on counseling theory and leveraging deep learning techniques,this study proposes an automated model for identifying service quality in online mental health services.Second,we analyze how the professional competence of early service providers influences the participation of subsequent providers,revealing the underlying mechanisms of participation from a peer effects perspective.Finally,this research investigates the intervention mechanisms of both individual and peer experiences on service quality,exploring the factors that drive quality improvement from a learning theory perspective.This research not only contributes to a deeper academic understanding of service quality in crowdsourced online mental health services,but also offers practical managerial insights for addressing challenges in service delivery.