Qin Wen, Dai Xiaoyun, Huang Peipei, Luo Jun, Shen Yang, Zhu Qin
Affiliated Hospital of Nantong University, No 20 Xisi Road, Nantong, Jiangsu, 226001, PR China.
BMC Nurs. 2025 Aug 12;24(1):1063. doi: 10.1186/s12912-025-03729-y.
The rapid rise of robot-assisted surgery (RAS), especially with the Da Vinci Surgical System (DVSS), has transformed surgical practices, enhanced precision and improving patient outcomes. As this technology becomes more prevalent, operating room nurses have taken on more specialized roles. However, there is a lack of standardized training and competency evaluation for these nurses, leading to inconsistencies in their preparedness.
The current study aimed at developing a competency evaluation index system for nurses in RAS: a Delphi study.
This study employed a modified Delphi method to develop a competency evaluation index system for nurses in RAS. The study was conducted across seven tertiary-level hospitals in China, all equipped with the Da Vinci Surgical System. Three groups of participants were involved: nursing educators and managers, surgeons, and an expert panel. Data were collected through a literature review, semi-structured interviews, and two rounds of Delphi expert consultations. The importance of competency indicators was measured using a 5-point Likert scale in the survey.
The positive coefficient of experts in both rounds of the Delphi survey was 100%, with an authority coefficient of 0.9125, the Kendall's coordination coefficients of the first, second, and third level indexes were 0.467, 0.324, and 0.260 (P < 0.001), 0.454, 0.257, and 0.331 (P < 0.001). The final index system includes three primary indicators (basic nursing Competencies, specialty nursing competencies and comprehensive application capabilities), twelve secondary indicators, and sixty-seven tertiary indicators.
This study established a structured competency evaluation framework for nurses in robot-assisted surgery, comprising three primary, twelve secondary, and sixty-seven tertiary indicators. This system serves as a foundational tool for assessing professional competencies and provides a reference for designing targeted training programs.
Future research should focus on converting the indicators into a scale for wider use, further validating its effectiveness and practicality.
Not applicable.
机器人辅助手术(RAS)的迅速兴起,尤其是达芬奇手术系统(DVSS)的应用,改变了手术方式,提高了手术精准度并改善了患者预后。随着这项技术日益普及,手术室护士承担了更专业化的角色。然而,针对这些护士缺乏标准化培训和能力评估,导致他们的准备情况存在差异。
本研究旨在开发一套机器人辅助手术护士能力评估指标体系:德尔菲研究。
本研究采用改良德尔菲法开发机器人辅助手术护士能力评估指标体系。研究在中国七家三级医院开展,这些医院均配备了达芬奇手术系统。参与研究的有三组人员:护理教育工作者和管理人员、外科医生以及一个专家小组。数据通过文献回顾、半结构化访谈以及两轮德尔菲专家咨询收集。在调查中,使用5点李克特量表衡量能力指标的重要性。
两轮德尔菲调查中专家的积极系数均为100%,权威系数为0.9125,一级、二级和三级指标的肯德尔协调系数分别为0.467、0.324和0.260(P<0.001),0.454、0.257和0.331(P<0.001)。最终指标体系包括三个一级指标(基础护理能力、专科护理能力和综合应用能力)、十二个二级指标和六十七个三级指标。
本研究为机器人辅助手术护士建立了一个结构化的能力评估框架,包括三个一级、十二个二级和六十七个三级指标。该体系作为评估专业能力的基础工具,为设计针对性培训项目提供了参考。
未来研究应聚焦于将这些指标转化为量表以便更广泛应用,进一步验证其有效性和实用性。
不适用。