Construction and Evaluation of a Predictive Model for Grassroots Nurses’ Risk Perception of “Internet + Nursing Services”: A Multicenter Cross-Sectional Study

Peiran Guo,1,2,&ast; Yuting Tan,1,2,&ast; Li Feng,3 Cui Liu,4 Jin Sun,5 Rong Cheng,1,2 Yanling Xiao,1 Xingxin Zhan,6 Lingjie Yang,1 Zhixia Zhang1 1Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, Hubei, People’s Republic of China; 2Wuhan University of Science and Technology School of Medicine, Institute of Nursing Research, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan, Hubei, People’s Republic of China; 3School of Public Health and Nursing, Hubei University of Science and Technology, Xianning, Hubei, People’s Republic of China; 4Geriatric Hospital Affiliated with Wuhan University of Science and Technology, Wuhan, Hubei, People’s Republic of China; 5Yiling Hospital of Yichang City, Yichang, Hubei, People’s Republic of China; 6School of Public Health, Xinyu University, Xinyu, Jiangxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhixia Zhang, Tianyou Hospital, Wuhan University of Science and Technology, No. 9, Tujialing, Dingzhiqiao, Wuchang District, Wuhan, Hubei, People’s Republic of China, Tel +8613476805827, Email [email protected]: The development of “Internet + nursing services” can effectively solve the problem of population aging, and grassroots nurses are the primary providers of such services in rural areas. This study aimed to analyze the factors affecting grassroots nurses’ risk perception of “Internet + nursing services” and construct a predictive model.Patients and Methods: A multicenter cross-sectional study of 2220 nurses from 27 secondary hospitals and 36 community health centers in Hubei Province was conducted from August to December 2023 using a multi-stage cluster sampling method. Information was collected through a structured anonymous questionnaire. A Chi-square test, a Welch t-test, and binary logistic regression analyses were employed to determine independent risk factors for grassroots nurses’ risk perception of “Internet + nursing services”, and a nomogram was constructed. Receiver operating characteristic curves, calibration curves, and decision curves were plotted to evaluate the discrimination, calibration, and clinical effectiveness of the nomogram.Results: A total of 2050 valid questionnaires were collected, demonstrating that 51.95% of grassroots nurses thought that “Internet + nursing services” was a medium-high risk. Age, other sources of income, knowledge about “Internet + nursing services”, personal safety, physical function, occupational exposure, social psychosocial, and time risk (P< 0.05) were independent risk factors for grassroots nurses’ risk perception. The area under the receiver operating characteristic curve of the nomogram was 0.939. The calibration and decision curve analyses demonstrated good calibration ability and clinical application values.Conclusion: The prediction model constructed in this study has good prediction ability. Most grassroots nurses believe that “Internet + nursing services” are risky and influenced by several factors. It is suggested that the government and hospitals should formulate a unified charging standard, improve the safety guarantee, and gradually eliminate the concerns of grassroots nurses.Keywords: grassroots nurse, Internet + nursing services, risk perception, predictive model