Wen-Yen Lo1, Li-Yin Chien2, Fang-Ming Hwang3, Nicole Huang4, Shu-Ti Chiou5,6. 1. Department of Nursing, Taipei City Hospital, Taipei, Taiwan. 2. Institute of Community Health Care, National Yang-Ming University, Taipei, Taiwan. 3. Department of Education, National Chiayi University, Chiayi, Taiwan. 4. Institute of Hospital and Health Care Administration, National Yang-Ming University, Taipei, Taiwan. 5. School of Medicine, National Yang-Ming University, Taipei, Taiwan. 6. Cheng Hsin General Hospital, Taipei, Taiwan.
Abstract
AIMS: The aim of this study was to examine the structural relationships linking job stress to leaving intentions through job satisfaction, depressed mood and stress adaptation among hospital nurses. BACKGROUND: High turnover among nurses is a global concern. Structural relationships linking job stress to leaving intentions have not been thoroughly examined. DESIGN: Two nationwide cross-sectional surveys of full-time hospital staff in 2011 and 2014. METHODS: The study participants were 26,945 and 19,386 full-time clinical nurses in 2011 and 2014 respectively. Structural equation modelling was used to examine the interrelationships among the study variables based on the hypothesized model. We used cross-validation procedures to ensure the stability and validity of the model in the two samples. RESULTS: There were five main paths from job stress to intention to leave the hospital. In addition to the direct path, job stress directly affected job satisfaction and depressed mood, which in turn affected intention to leave the hospital. Stress adaptation mitigated the effects of job stress on job satisfaction and depressed mood, which led to intention to leave the hospital. Intention to leave the hospital preceded intention to leave the profession. Those variables explained about 55% of the variance in intention to leave the profession in both years. CONCLUSION: The model fit was good for both samples, suggesting validity of the model. Strategies to decrease turnover intentions among nurses could focus on creating a less stressful work environment, increasing job satisfaction and stress adaptation and decreasing depressed mood. Hospitals should cooperate in this issue to decrease nurse turnover.
AIMS: The aim of this study was to examine the structural relationships linking job stress to leaving intentions through job satisfaction, depressed mood and stress adaptation among hospital nurses. BACKGROUND: High turnover among nurses is a global concern. Structural relationships linking job stress to leaving intentions have not been thoroughly examined. DESIGN: Two nationwide cross-sectional surveys of full-time hospital staff in 2011 and 2014. METHODS: The study participants were 26,945 and 19,386 full-time clinical nurses in 2011 and 2014 respectively. Structural equation modelling was used to examine the interrelationships among the study variables based on the hypothesized model. We used cross-validation procedures to ensure the stability and validity of the model in the two samples. RESULTS: There were five main paths from job stress to intention to leave the hospital. In addition to the direct path, job stress directly affected job satisfaction and depressed mood, which in turn affected intention to leave the hospital. Stress adaptation mitigated the effects of job stress on job satisfaction and depressed mood, which led to intention to leave the hospital. Intention to leave the hospital preceded intention to leave the profession. Those variables explained about 55% of the variance in intention to leave the profession in both years. CONCLUSION: The model fit was good for both samples, suggesting validity of the model. Strategies to decrease turnover intentions among nurses could focus on creating a less stressful work environment, increasing job satisfaction and stress adaptation and decreasing depressed mood. Hospitals should cooperate in this issue to decrease nurse turnover.
Authors: Jianfeng Li; Hongping Liu; Beatrice van der Heijden; Zhiwen Guo Journal: Int J Environ Res Public Health Date: 2021-01-15 Impact factor: 3.390
Authors: Ana María Porcel-Gálvez; Sergio Barrientos-Trigo; Sara Bermúdez-García; Elena Fernández-García; Mercedes Bueno-Ferrán; Bárbara Badanta Journal: Int J Environ Res Public Health Date: 2020-11-15 Impact factor: 3.390