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The effects of emotional labor and competency on job satisfaction in nurses of China: A nationwide cross-sectional survey

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INTERNATIONAL JOURNAL OF NURSING SCIENCES
卷 5, 期 4, 页码 383-389

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ELSEVIER
DOI: 10.1016/j.ijnss.2018.08.001

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Competency; Emotional labour; Job satisfaction; Nurse

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Objectives: This study aimed to explore the status of job satisfaction, emotional labour, core competencies and job stress and the associations of emotional labour and core competencies with the job satisfaction of nurses in China. Methods: Data were collected by using a self-designed general information questionnaire, a job satisfaction questionnaire (McCloskey/Mueller Satisfaction Scale), a nurse emotional labour questionnaire, the Competency Inventory for Registered Nurses questionnaire, and a nurse job stressor questionnaire. A total of 13,448 nurses from 92 hospitals across mainland of China were surveyed, and 11,337 respondents (84.3% response rate) completed the questionnaires. Results: The survey results indicated that Chinese nurses had average job satisfaction (24.77 +/- 5.23), moderate job stress (86.84 +/- 21.12), moderate to high emotional labour experiences (55.08 +/- 9.63) and high competency (195.77 +/- 37.61). Multiple linear regression indicated that surface acting was negatively correlated with job satisfaction (P < 0.01), while deep acting (P < 0.01) and nurses' core competence (P < 0.01) were positively associated with job satisfaction. Besides, The results also indicated job stress, geographical distribution, hospital category, gender, work shift, only children, parenting status, monthly salary, nursing experience, professional title, hospital area were the influencing factors of job satisfaction (P < 0.05). Conclusion: The improvement of the emotional labour and competency of nurses may help enhance their job satisfaction. (c) 2018 Chinese Nursing Association. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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