درخت حوزه‌های تخصصی

Artificial Intelligence has is a pivotal tool in enhancing crisis management during natural disasters such as earthquakes. This article examines the role of AI in generating real-time information, predicting stakeholder and population responses, and optimizing decision-making processes. A meta-analysis of existing literature highlights AI's potential to transform traditional crisis management approaches by enabling rapid assessment of disaster scenarios, efficient resource allocation, and communication strategies tailored to situational dynamics. However, the integration of AI in such critical domains requires not only technological advancement but also a paradigm shift in the skills and competencies of crisis management professionals. This paper argues for the necessity of continuous education for stakeholders, emphasizing training in AI tools and data literacy to ensure effective utilization of these technologies in high-stakes environments. Furthermore, it explores the ethical implications of AI use in crisis contexts, including issues of accountability, data privacy, and bias mitigation. To address these challenges through sustained education and interdisciplinary collaboration, stakeholders should harness AI’s full potential while minimizing risks.

Purpose: This present study is dealing with presenting a model for evaluating the effectiveness of short and medium-term training courses at Mazandaran University of Medical Sciences. Methodology : In this research, a quantitative research method was used. The statistical population consists of specialists and university administrators and staff of the Medical Sciences Organization in Mazandaran Province. The purposive sampling method was applied in the qualitative phase; moreover, the proportional stratified sampling method was used in the quantitative phase. Furthermore, the data collection was done through field interviews and researcher-made questionnaires. The collected responses were analyzed by using descriptive statistics (frequency, percentage, mean, standard deviation) and inferential statistics (correlation and independent t-tests) using SPSS and Lisrel. Findings : The main components of short- and medium-term university courses consist (effectiveness and efficiency, job satisfaction, motivation and morale, planning and goal setting, managerial skills, institutionalizing organizational goals, managerial communication skills, and finally emphasis on education and development). Conclusion : In addition, according to the results, in rating, the short-term and medium-term dimensions of university courses, among all the institutional and operational levels, the factors (the effectiveness and efficiency, job satisfaction, motivation and morale, planning and goal setting, managerial skills, institutionalizing organizational goals, managerial communication skills, and the emphasis on education and development) were respectively rated as the most and the least significance in short- and medium-term university courses