AIM: To explore nurses' views and experiences regarding job satisfaction and their intention to leave in Shanghai. BACKGROUND: The widespread nursing shortage and nurses' high turnover rate has become a global issue. Job satisfaction is the most consistent predictor of nurses' intention to leave and has been reported as explaining most of the variance on their intention to leave. DESIGN: A cross-sectional survey. METHODS: A convenience sample of nurses was recruited from 19 large general hospitals in Shanghai and the research assistants distributed 2850 questionnaires to nurses. The response rate of this study was 78·95%, and 2250 nurses completed the questionnaire. RESULTS: Regarding the overall job satisfaction, 50·2% nurses were dissatisfied and 40·4% nurses reported that they had intention to leave the current employment. Respondents' characteristics had an impact on job satisfaction and their intention to leave. The results showed that age, marital status, work experience, overall job satisfaction, job satisfaction: extrinsic rewards, interaction, praise/recognition and control/responsibility were significant factors contributing to nurses' intention to leave. CONCLUSIONS: The finding may be a cause of concern for hospital management and highlights the importance of the two concepts (job satisfaction and intention to leave) in Shanghai. Innovative and adaptable managerial interventions need to be taken to improve nurses' job satisfaction and to strengthen their intention to stay. RELEVANCE TO CLINICAL PRACTICE: Our findings outline some issues contributing to these problems and provide nurse administrators with information regarding specific influences on nurses' job satisfaction and intention to leave in Shanghai and innovative and adaptable managerial interventions that are needed. Our findings may also provide direction for nurse managers and healthcare management to implement strategies to improve nurses' job satisfaction and their intention to stay.
AIM: To explore nurses' views and experiences regarding job satisfaction and their intention to leave in Shanghai. BACKGROUND: The widespread nursing shortage and nurses' high turnover rate has become a global issue. Job satisfaction is the most consistent predictor of nurses' intention to leave and has been reported as explaining most of the variance on their intention to leave. DESIGN: A cross-sectional survey. METHODS: A convenience sample of nurses was recruited from 19 large general hospitals in Shanghai and the research assistants distributed 2850 questionnaires to nurses. The response rate of this study was 78·95%, and 2250 nurses completed the questionnaire. RESULTS: Regarding the overall job satisfaction, 50·2% nurses were dissatisfied and 40·4% nurses reported that they had intention to leave the current employment. Respondents' characteristics had an impact on job satisfaction and their intention to leave. The results showed that age, marital status, work experience, overall job satisfaction, job satisfaction: extrinsic rewards, interaction, praise/recognition and control/responsibility were significant factors contributing to nurses' intention to leave. CONCLUSIONS: The finding may be a cause of concern for hospital management and highlights the importance of the two concepts (job satisfaction and intention to leave) in Shanghai. Innovative and adaptable managerial interventions need to be taken to improve nurses' job satisfaction and to strengthen their intention to stay. RELEVANCE TO CLINICAL PRACTICE: Our findings outline some issues contributing to these problems and provide nurse administrators with information regarding specific influences on nurses' job satisfaction and intention to leave in Shanghai and innovative and adaptable managerial interventions that are needed. Our findings may also provide direction for nurse managers and healthcare management to implement strategies to improve nurses' job satisfaction and their intention to stay.
Authors: Abdul Kadar Muhammad Masum; Md Abul Kalam Azad; Kazi Enamul Hoque; Loo-See Beh; Peter Wanke; Özgün Arslan Journal: PeerJ Date: 2016-04-26 Impact factor: 2.984
Authors: Huixuan Zhou; Xueyan Han; Juan Zhang; Jing Sun; Linlin Hu; Guangyu Hu; Shichao Wu; Pengyu Zhao; Feng Jiang; Yuanli Liu Journal: Int J Environ Res Public Health Date: 2018-07-19 Impact factor: 3.390
Authors: Jessica Scharf; Patricia Vu-Eickmann; Jian Li; Andreas Müller; Peter Angerer; Adrian Loerbroks Journal: Int J Environ Res Public Health Date: 2019-06-26 Impact factor: 3.390