Profiles of innovative behavior and associated predictors among clinical nurses: a multicenter study using latent profile analysis

被引:1
作者
Li, Husheng [1 ,2 ]
Qiao, Yue [1 ,2 ]
Wan, Tianxiang [3 ,4 ]
Shao, Chun Hua [4 ]
Wen, Fule [2 ]
Liu, Xiaoxin [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai Chest Hosp, Dept Nursing, Sch Med, 241 West Huaihai Rd, Shanghai 200030, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Nursing, 227 South Chongqing Rd, Shanghai 200025, Peoples R China
[3] Univ Manchester, Fac Biol Med & Hlth, Dept Hlth Sci, Manchester M13 9PT, England
[4] Northumbria Univ, Dept Nursing Midwifery & Hlth, Newcastle Upon Tyne NE7 7XA, England
关键词
Innovative behavior; Clinical nurses; Future time perspective; Work engagement; Nursing management; Latent profile analysis; Logistic regression analysis; WORK ENGAGEMENT; TIME; FUTURE; QUESTIONNAIRE; PERSPECTIVE; CHINA; MODEL;
D O I
10.1186/s12912-025-02716-7
中图分类号
R47 [护理学];
学科分类号
1011 ;
摘要
Background Innovative Behavior (IB) is a key prerequisite for nurses in solving clinical problems. However, existing research on IB among clinical nurses is relatively limited. Objective To identify profiles and characteristics of IB among clinical nurses and explore the associated predictors, as well as the relationships with research outputs. Methods A multicenter cross-sectional study was conducted on 354 clinical nurses in Shanghai from April 2023 to May 2023 (response rate 98.06%). IB was measured by the Innovative Behavior Scale for Nurses (IBSN), future time perspective was measured by the Future Time Perspective Scale (FTPS), and work engagement was measured by the Utrecht Work Engagement Scale-9 (UWES-9). Socio-demographic and professional data and research output indicators were measured by a self-designed questionnaire. We used latent profile analysis (LPA) by Mplus 7.0 to identify latent classes of IB. Ordinal logistic regression analysis was used to analyze the relevant predictors on the different profiles. And then Pearson's chi-squared was used to analyze the association between IB level and research output. Results Among the respondents, individuals aged 25 to 35 accounted for 55.9%, and females comprised 94.6%. IB of clinical nurses can be identified into 3 groups: low-level (n = 108, 30.51%), moderate-level (n = 149, 42.09%), and high-level (n = 97, 27.40%) groups. Based on the results of LPA, marital status, education level, work experience, monthly income, night shifts, future time perspective scores, and work engagement scores can be the predictors of IB among different profiles. Statistically significant associations were found between IB level and research productivity, including publishing academic papers (chi(2 )= 15.307, p < 0.001), registering patents (chi(2 )= 17.163, p < 0.001), and winning Sci. & Tech awards (chi(2 )= 27.814, p < 0.001). Conclusion According to our research, clinical nurses have three unique IB profiles. The current level is predominantly at a moderate level, with less than 30% demonstrating a high level of innovation. It revealed that better socio-demographic status and professional characteristics, future time perspective, and work engagement positively influenced innovative behavior among clinical nurses. The findings also highlight the potentially important role of IB in contributing to nurses' research output. Practical implications As far as we know, it might be the first study to employ LPA to clarify the heterogeneity in the levels of IB and their specific distribution among nurses. Our findings may provide a new viewpoint for promoting IB among clinical nurses. Nursing administrators should pay attention to IB of clinical nurses and develop targeted interventions to enhance their IB levels.
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页数:13
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