Huong Thi Thu Nguyen1, Kazuyo Kitaoka2, Masune Sukigara3, Anh Lan Thai4. 1. Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan. Electronic address: nguyenthuhuongdhyhp@gmail.com. 2. Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan. 3. Department of Humanities and Social Sciences, Nagoya City University, Nagoya, Japan. 4. Nursing Faculty of Hai Phong University for Medicine and Pharmacy, Haiphong, Viet Nam.
Abstract
PURPOSE: This study aimed to create a Vietnamese version of both the Maslach Burnout Inventory-General Survey (MBI-GS) and Areas of Worklife Scale (AWS) to assess the burnout state of Vietnamese clinical nurses and to develop a causal model of burnout of clinical nurses. METHODS: We conducted a descriptive design using a cross-sectional survey. The questionnaire was hand divided directly by nursing departments to 500 clinical nurses in three hospitals. Vietnamese MBI-GS and AWS were then examined for reliability and validity. We used the revised exhaustion +1 burnout classification to access burnout state. We performed path analysis to develop a Vietnamese causal model based on the original model by Leiter and Maslach's theory. RESULTS: We found that both scales were reliable and valid for assessing burnout. Among nurse participants, the percentage of severe burnout was 0.7% and burnout was 15.8%, and 17.2% of nurses were exhausted. The best predictor of burnout was "on-duty work schedule" that clinical nurses have to work for 24 hours. In the causal model, we also found similarity and difference pathways in comparison with the original model. CONCLUSION: Vietnamese MBI-GS and AWS were applicable to research on occupational stress. Nearly one-fifth of Vietnamese clinical nurses were working in burnout state. The causal model suggested a range of factors resulting in burnout, and it is necessary to consider the specific solution to prevent burnout problem.
PURPOSE: This study aimed to create a Vietnamese version of both the Maslach Burnout Inventory-General Survey (MBI-GS) and Areas of Worklife Scale (AWS) to assess the burnout state of Vietnamese clinical nurses and to develop a causal model of burnout of clinical nurses. METHODS: We conducted a descriptive design using a cross-sectional survey. The questionnaire was hand divided directly by nursing departments to 500 clinical nurses in three hospitals. Vietnamese MBI-GS and AWS were then examined for reliability and validity. We used the revised exhaustion +1 burnout classification to access burnout state. We performed path analysis to develop a Vietnamese causal model based on the original model by Leiter and Maslach's theory. RESULTS: We found that both scales were reliable and valid for assessing burnout. Among nurse participants, the percentage of severe burnout was 0.7% and burnout was 15.8%, and 17.2% of nurses were exhausted. The best predictor of burnout was "on-duty work schedule" that clinical nurses have to work for 24 hours. In the causal model, we also found similarity and difference pathways in comparison with the original model. CONCLUSION: Vietnamese MBI-GS and AWS were applicable to research on occupational stress. Nearly one-fifth of Vietnamese clinical nurses were working in burnout state. The causal model suggested a range of factors resulting in burnout, and it is necessary to consider the specific solution to prevent burnout problem.
Authors: Emilia I De la Fuente-Solana; Nora Suleiman-Martos; Laura Pradas-Hernández; Jose L Gomez-Urquiza; Guillermo A Cañadas-De la Fuente; Luis Albendín-García Journal: Int J Environ Res Public Health Date: 2019-07-19 Impact factor: 3.390
Authors: Thi Hong Thai Bui; Thi Minh Duc Tran; Thi Nhu Trang Nguyen; Thy Cam Vu; Xuan Diep Ngo; Thi Hang Phuong Nguyen; Thi Le Hang Do Journal: Health Psychol Behav Med Date: 2022-01-05
Authors: Natsu Sasaki; Kotaro Imamura; Tran T T Thuy; Kazuhiro Watanabe; Nguyen T Huong; Kazuto Kuribayashi; Asuka Sakuraya; Bui M Thu; Nguyen T Quynh; Nguyen T Kien; Nguyen T Nga; Nguyen T H Giang; Truong Q Tien; Harry Minas; Melvyn Zhang; Akizumi Tsutsumi; Norito Kawakami Journal: J Occup Health Date: 2019-09-21 Impact factor: 2.708