移情
护理部
保密
仿人机器人
心理学
背景(考古学)
定性研究
知情同意
医疗保健
相关性(法律)
定性性质
数据收集
机器人
应用心理学
医学
社会心理学
计算机科学
人工智能
社会学
政治学
古生物学
病理
机器学习
生物
法学
替代医学
计算机安全
社会科学
作者
Özen İnam,Yahya Kahvecioğlu
标识
DOI:10.1177/09697330251339416
摘要
Background This study explores intergenerational perspectives on the use of humanoid nurse robots in healthcare settings, recognizing the increasing relevance of robotic technologies and associated ethical, emotional, and privacy concerns. Research aim The study aims to assess acceptance levels, concerns, and expectations regarding humanoid nurse robots among Generations X, Y, and Z. Research design A mixed-method design combining quantitative survey analysis and qualitative interviews was employed. Participants and research context The study was conducted in Türkiye with 45 participants: 15 from Generation X (1965–1980), 15 from Generation Y (1981–1996), and 15 from Generation Z (1997–2012). Visual scenarios depicting robotic nurse applications were used during data collection. Ethical considerations The research obtained ethical approval from the Maltepe University Ethics Committee with the decision number 2024/23-02, issued during the meeting held on December 12, 2024 (Meeting No: 2024/23). Informed consent was obtained from all participants, and confidentiality and voluntary participation were ensured. Findings Quantitative findings showed that Generations Y and Z were more accepting of robots in technical tasks, whereas Generation X expressed skepticism, especially in emergency care roles. Privacy concerns were high across all groups (mean = 4.2). Qualitative interviews revealed that 75% of participants were skeptical about robots’ lack of empathy and strongly opposed their use in birth and neonatal care, emphasizing that these emotionally sensitive areas require the compassionate presence and emotional intelligence of human nurses. Conclusions: Cultural and generational characteristics significantly affect the acceptance of humanoid nurse robots. Targeted education, stronger data privacy frameworks, and emotionally intelligent human-robot interaction strategies are essential for successful integration into healthcare. The study confirms the Robot Anxiety Scale’s validity and reliability in the Turkish context.
科研通智能强力驱动
Strongly Powered by AbleSci AI