Evidence map›Paper›PMID 40797327›Full record

ArticleBMC nursing2025

Development of a core competency evaluation index system for specialist nurses in robot-assisted surgery: a Delphi study.

Wen Qin, Xiaoyun Dai, Peipei Huang, Jun Luo, Yang Shen, Qin Zhu

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Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Wen QinAffiliated Hospital of Nantong University, No 20 Xisi Road, Nantong, Jiangsu, 226001, PR China.
Xiaoyun DaiAffiliated Hospital of Nantong University, No 20 Xisi Road, Nantong, Jiangsu, 226001, PR China.
Peipei HuangAffiliated Hospital of Nantong University, No 20 Xisi Road, Nantong, Jiangsu, 226001, PR China.
Jun LuoAffiliated Hospital of Nantong University, No 20 Xisi Road, Nantong, Jiangsu, 226001, PR China.
Yang ShenAffiliated Hospital of Nantong University, No 20 Xisi Road, Nantong, Jiangsu, 226001, PR China.
Qin ZhuAffiliated Hospital of Nantong University, No 20 Xisi Road, Nantong, Jiangsu, 226001, PR China. tdfyzhuqin@163.com.

Funding

Nantong Municipal Health Commission Research Project MS2024007Social Livelihood Science and Technology Fund of Nantong MS22022038
6 · The paper itself

Abstract

backgroundThe 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.

aimThe current study aimed at developing a competency evaluation index system for nurses in RAS: a Delphi study.

methodsThis 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.

resultsThe 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.

conclusionThis 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. RECOMMENDATION: Future research should focus on converting the indicators into a scale for wider use, further validating its effectiveness and practicality. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Core competencyDelphi studyIndex systemRobot-assisted surgerySpecialist nurse

Identifiers

PMID40797327
PMCPMC12345079

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.