درخت حوزه‌های تخصصی

English for Academic Purposes (EAP) courses are currently well-established university programs. These courses are run independently by English Language Teaching (ELT) instructors and content instructors without any collaboration. However, ELT instructors and content instructors do not receive the same level of collegiality and social support from the organizations and students. This paper probed burnout among Iranian EAP teachers, including content instructors and ELT instructors in 28 state universities and its variations in relation to their demographic and organizational characteristics. To this aim, the Persian version of the Maslach Burnout Inventory (MBI) was administered to content instructors (N=185) and ELT instructors (N=86) in the state universities in Iran. The results of the study indicated that while most of EAP teachers, both content instructors and ELT instructors, had low burnout, a considerable number had mid-levels of emotional exhaustion and personal accomplishment. The findings of the study also indicated that the ELT instructors had higher emotional exhaustion than the content instructors, and it was also found that the content instructors with more than 13 years of experience and the ELT instructors with more than 20 years of experience in teaching such courses had the lowest burnout. Based on the findings of the study, educational administrators are suggested to take remedial and preventive actions against EAP teachers’ burnout and enhance ELT instructors’ occupational well-being. It also seems necessary to assist EAP teachers in adapting to the requirements of teaching EAP courses through pre/in-service teacher training courses to obviate the need for extensive experience for gaining expertise in it.

Purpose: The purpose of this study was to present a comprehensive model of empowerment in the Social Security Organization. Methodology: This research was based on the applied purpose and in terms of quantitative and qualitative research method (mixed) and in terms of data collection was descriptive-survey. The statistical population of the study consisted of two groups of experts and thinkers. The first group consisted of university professors whose field of study and field of study was related to the field of research and the second group included organizational experts with a master's degree or higher with managerial and experimental backgrounds related to human resources in the qualitative section after counting the criteria and indicators extracted. From the theoretical foundations, a researcher-made questionnaire based on the Looshe and Likert spectrum was prepared and after passing the Delphi survey, a conceptual model was extracted. Then, in the quantitative part, exploratory factor analysis method was used to identify the model factors and confirmatory factor analysis method was used to fit the model. In order to check the validity of the questions, purposive sampling method was used among organizational and academic experts, of which 15 people were included as a statistical sample. In the second and third stages of validity of the questions, to strengthen the screening process, the snowball method was used to compile the sample, which after saturating the data of 32 final experts for the exploration section and building a conceptual model participated. Statistical analysis was performed using SPSS software to cluster the indicators and create research components and Smart PLS software to confirm the research model. Findings: The results of this study identified 11 components and 37 indicators as the most important factors of comprehensive empowerment and showed that individuals will be empowered individually when intra-individual intelligence, creativity-oriented education, psychological self-knowledge, internal performance, knowledge-based decision making and individual self-efficacy. Together, they will be empowered in the collective, team, and organizational spheres when the culture of bureaucracy, internal reward, fundamental self-efficacy, and interactive policy come together. Conclusion: After exploratory factor analysis and component classification, the fit of the model obtained from the previous step was investigated and confirmed with a good fit.