Psychological Distress, Job Satisfaction, And Burnout Among Mizan- Teppi University Academic Staffs.

Abstract

The main objective of the study was assessing psychological distress, job satisfaction, and burnout among Mizan-Teppi University academic staffs. To achieve the objective of the study, cross sectional survey research design was employed. The study population were total of four hundred twenty nine (429), Mizan campus academic staffs. Among this, one hundred eighty (180) samples were participated in the study. Samples were selected using proportionate stratified random sampling. To collect data from the participants, Kessler psychological distress scale, Minnesota Job Satisfaction questionnaire, and Maslach Burnout inventory were used. Quantitatively collected data was analysed by descriptive and inferential statistics such as mean, standard deviation, percentage, independent samples t test, one way Anova and multiple linear regression. Data collected through an interview was analysed thematically. The result of the study depicts that majority of the participants (35%) had moderate psychological distress Majority of the participants (46%) were moderately satisfied by their job. Forty eight (48%) of the participants had high emotional exhaustion, 45.6% of participants had depersonalisation, and 52% of participants had low personal accomplishment. The finding of this study showed that there is no statistically significant difference on all dimensions of burnout (emotional exhaustion, depersonalisation, and personal accomplishment) based on demographic variables. Teaching experience, psychological distress and job satisfaction were significant predictors of emotional exhaustion whereas depersonalisation was predicted by teaching experience and job satisfaction. Positive interaction, withdrawal, relaxation, and religious practices were used by participants to handle burnout. The university should take action to minimize psychological distress and burnout, should give training focused at problem solving and stress management.