Disaster Medicine Training for Pre-Clinical Medical Students at the University of Tripoli, Libya

Libya has faced several natural and human-made disasters in recent years, highlighting the need to strengthen disaster preparedness and response capacity. Storm Daniel in Derna in 2023 caused severe flooding, loss of life, and large-scale displacement. Other Libyan communities have also been affected by floods, water-related hazards, armed conflict, landmines, and unexploded ordnance. These events show the vulnerability of local communities and the need for structured disaster medicine education, especially among medical students who may contribute to emergency response in future crises. This study evaluates the effectiveness of a structured disaster medicine training program for pre-clinical medical students at the Faculty of Medicine, University of Tripoli. The program includes both theoretical and practical components delivered by university faculty with experience in clinical skills and emergency response, together with members of the Libyan Red Crescent who have extensive field experience in disasters and emergencies. Training topics include disaster concepts, disaster response, first aid, psychological support, emergency communication and coordination, prevention of disease outbreaks, volunteer safety, environmental sanitation, landmine and war-remnant awareness, and safe and respectful management of deceased persons. The study uses a Randomized Controlled Trial (RCT) design. Eligible students are assigned to either an intervention group, which receives the disaster medicine training, or a control group, which does not receive the training during the study period. The intervention group receives approximately 20 to 30 hours of structured training. Learning outcomes are assessed using a baseline knowledge test, a mid-training test, and an Objective Structured Clinical Examination (OSCE). The OSCE scenarios are reviewed by emergency physicians, disaster medicine experts, and university faculty. Student assessments are scored using coded identifiers to reduce b