Interactive Effect of Background Variables and Workload Parameters on the Quality of Life among Nurses Working in Highly Complex Hospital Units: A Cross-sectional Study LC08-LC13
Instructor, Department of Occupational Health Engineering, School of Public Health,
Zabol University of Medical Sciences, Zabol, Iran.
E-mail: firstname.lastname@example.org; email@example.com
Introduction: Quality of Working Life (QWL) is a vital concept in the employees’ life which can confirm the efficiency of the organisation and job satisfaction of employees.
Aim: The present study investigated the interactive effects of background variables (job and demographic characteristics) and workload parameters on the Work-Related Quality of Life (WRQoL) among nurses working in highly complex hospital units (ICU, CCU and Emergency).
Materials and Methods: This was a cross-sectional study conducted in 2017, among all male and female volunteer nurses (n=840). For this purpose, NASA-Task Load Index (NASA-TLX) and WRQoL were used. A general questionnaire was also used to collect the job and demographic information of nurses. The statistical analyses were performed through MANOVA, tests between-subject’s effects, Box’s M, pair-wise comparisons, Bonferroni method, and multiple regression.
Results: The findings indicated that the difference in mean score of WRQoL was statistically significant in all groups of the studied background variables except gender and Body Mass Index (BMI). The relationship between the variables of age, work experience, and the number of patients per shift and scores of work-related quality of life was statistically significant. Also, based on the results of multivariate regression analysis, the variables of overtime hours, the number of patients per shift, age, and the level of workload remained in the regression model and their coefficients of influence were estimated -43%, -23%, -19%, and -15%, respectively.
Conclusion: The results indicated the simultaneous effect of the studied variables on the nurses’ WRQoL. Of these, the variables of overtime hours, the number of patients per shift, age, and workload level were finally kept in the regression model to explain the most percentage of changes in the WRQoL.