Determinants of Job Satisfaction among health workforce in Menelik II Referral Hospital, Addis Ababa, Ethiopia
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Date
2019-09
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A.A.U
Abstract
Health workforce job satisfaction is a crucial element of health care service provision. However, determinants of job satisfaction among health workforce are not well studied in referral hospitals using econometric models. The objective of this study was to identify the key determinants of job satisfaction among health workforce in Menelik II Referral Hospital, Addis Ababa, Ethiopia.
The study used a cross sectional survey design. Structured interview with 250 sample respondents were held, who were selected using probability sampling technique. Descriptive statistics and inferential statistics: ordered logistic regression was used to identify sociodemographic and work related determinants affecting job satisfaction of health work force and data analysis at institutional level.
The result show that most of the respondents are in a very satisfied and satisfied category of job satisfaction. In which, the distribution shows 14.69% and 29.8% of the total respondents are very satisfied and satisfied with their job respectively. On the other hand 24.08% and 4.08% of the respondents are unsatisfied and very unsatisfied with their job respectively.
Among the study variables included in this study age, family size, educational status, income, professional category, year of experience, participation in decision making and relationship with supervisors are significantly determining the job satisfaction of health work force in the study area.
Thus, in the process of human resource management and development policy making process, these variables should be considered by the health sector and concerned decision makers and policy makers of health institutions.
Description
A Thesis Submitted to the Department of Master of Business Administration, Addis Ababa University in Partial Fulfillment for the Requirement of MBA Degree in Business Administration
Keywords
Health workforce, Menelik II, Ordered logistic regression