Emergency Responders Research Articles

Analyzing the use of fact-checking tools in disaster-risk reduction in Europe through the lens of the heuristic-systematic model

Journalists, emergency responders, and the general public facing natural and anthropogenic disasters frequently disseminate emergency information via social media. The spread of fake news during disasters can, however, disrupt the crisis management process and increase victim numbers. Identifying false information can curb its spread and reduce its impact on people’s attitudes and behaviors. Understanding how and why people in a disaster situation use fact-checking tools is crucial, as disaster-risk messages containing false content can usually be detected using systematic or heuristic processing. This study applies the heuristic-systematic model (HSM) to analyze social media users’ intention to use fact-checking tools. The empirical study data derived from 202 questionnaires collected through an online survey of residents of countries of the European Union. The results of structural equation modeling show the credibility of using HSM to analyze the intention to use fact-checking tools. About 33% of the changes in people’s intention to use fact-checking tools are predicted by this model. This study has implications for the use of theoretical models in communication science to predict intention to use fact-checking tools in disaster risk-reduction situations.

An IOT-Based Smart Helmet for Riding Security and Emergency Notification

Abstract: This project is designed to enhance rider safety by implementing a smart helmet system equipped with real-time monitoring and accident detection capabilities. The smart helmet integrates a range of sensors and modules that collectively work to ensure the rider’s well-being while on the road. At the core of the system is the ESP32 microcontroller, which serves as the brain of the helmet. It handles the data collected from various sensors, processes it, and manages communication with external devices via built-in Wi-Fi and Bluetooth modules. The ESP32 ensures fast and efficient data handling, which is crucial in emergency situations where every second counts. A piezoelectric sensor and a vibration sensor are used to detect any sudden impact or crash. These sensors are sensitive to pressure and motion, respectively, and can determine whether the rider has experienced an accident. Once an abnormal impact is detected, the system automatically initiates a safety protocol. To further ensure road safety, the helmet is equipped with a gas sensor, specifically designed to detect the presence of alcohol. This feature helps in preventing intoxicated driving by alerting the system or even restricting the functionality of the vehicle if alcohol levels are above permissible limits. A MEMS (Micro-Electro-Mechanical Systems) sensor is included to monitor the rider’s posture and body orientation. This allows the system to detect falls or unnatural body positions that might indicate a crash or loss of balance. When such a condition is detected, the system acts immediately to alert emergency contacts. Once any accident or abnormal condition is identified, the helmet uses a GPS module to determine the precise location of the rider. The real-time location data is then transmitted via the ESP32 to a connected mobile device, which can alert family members, emergency responders, or nearby authorities. This smart helmet not only provides accident detection but also offers live tracking of the rider, ensuring they are never out of reach. In addition, the integration of wireless communication enables remote monitoring and timely alerts, which is essential for quick medical or rescue responses. Overall, this intelligent helmet system represents a significant step forward in motorbike safety technology. By combining sensor technology with real-time communication, the project ensures immediate attention in the case of accidents, reducing the response time and potentially saving lives.

IOT-Based Headwater Detector System

The tragic headwater surge incident at Jeram Air Putih, Kemaman, which resulted in the loss of ten lives, has underscored the urgent necessity for real-time, technology-enhanced monitoring systems in high-risk recreational water areas. This research introduces a comprehensive Internet of Things (IoT)-based Headwater Monitoring System (HMS) designed to rectify the shortcomings of the manual observation techniques currently utilized in 39 identified high-risk regions throughout Malaysia. The system's architecture features a distributed network of environmental sensors, including ultrasonic level sensors, pressure transducers, and flow rate meters, strategically positioned at critical upstream sites. These sensors connect to edge computing units for initial data filtering and compression before wirelessly transmitting the information to a centralized cloud-based analytics platform using LoRaWAN and cellular IoT protocols (NB-IoT/LTE-M). Real-time hydrological data is analyzed through anomaly detection algorithms and threshold-based decision models, facilitating the prompt identification of sudden water level increases or flash flood indicators. When critical events are detected, the system activates automated alerts through a multi-channel communication framework, which includes SMS, mobile applications, and integration with local siren systems, aimed at informing authorities, emergency responders, and nearby residents. Field tests conducted under various rainfall conditions validate the system's responsiveness, energy efficiency, and scalability. This IoT-driven HMS represents a significant improvement in disaster risk reduction strategies, bolstering early warning capabilities and operational preparedness in flood-prone recreational areas.

Evaluation of a Digital Map Platform for Team Situational Awareness

Currently, emergency responders are utilizing several different digital platforms with limited interoperability. This can lead to miscommunication and misunderstandings between emergency responders. The digital map software Square is designed to mitigate this by providing a unified platform for emergency responders to facilitate sharing of information. With a large and diverse potential user group, it is important that the system is accessible and intuitive to use for as many users as possible, particularly in high-stress situations. The user testing and interview in this study provides insights into how new users interact with the software. The Square platform presented significant barriers to efficient use, and the findings from this study are used to improve the usability and accessibility in the forthcoming updates to Square.

Clinical Disorders in Cystic Fibrosis That Affect Emergency Procedures-A Case Report and Review.

Cystic fibrosis (CF) is a multisystemic disease caused by a genetic defect, namely a mutation in the CFTR gene, that results in the production of an abnormal protein that regulates the flow of chloride ions through epithelial cells, leading to the dehydration of secreted mucus and changes in its biological properties. Chronic inflammation and recurrent respiratory infections progressively damage lung tissue, leading to respiratory and cardiorespiratory failure. This study aims to present a clinical case and explore the clinical changes in CF that may influence the provision of pre-hospital first aid. The study presents a case report of a 23-year-old CF patient undergoing evaluation for lung transplantation, infected with Pseudomonas aeruginosa and Staphylococcus aureus with the MSSA phenotype, and in a severe condition due to infectious exacerbation. Despite antibiotic treatment, the patient's condition deteriorated, leading to respiratory failure and cardiac arrest. Emergency measures were taken to maintain airway patency-the patient was sedated, intubated, and connected to a ventilator. CF involves systemic complications that, during exacerbations, may require urgent interventions. Cystic fibrosis is associated with multiple systemic complications, some of which may, during exacerbations, require emergency medical interventions. Providing care to this patient group involves specific procedures addressing the consequences of the underlying disease. Due to increasing survival rates and the emergence of new phenotypes, there is a need for the continuous education of medical personnel, including emergency responders, regarding the management of genetically determined diseases. This study underscores the importance of recognizing CF's complex nature and adapting emergency care accordingly to ensure timely and effective intervention in life-threatening situations.

Association between obstructive sleep apnea and hearing loss among a cohort of emergency responders

PurposeWe sought to determine whether risk for obstructive sleep apnea (OSA) and OSA severity are associated with sensorineural hearing loss (HL) among emergency responders.MethodsWe evaluated two independent variables: OSA risk, categorized using Berlin Questionnaire criteria, and OSA severity, determined by polysomnogram (PSG) apnea-hypopnea indices (AHI). Logistic regression, adjusted for confounders, was used to assess the association between each OSA exposure and the outcome of HL among a cohort of emergency responders.ResultsThe study cohort included 13,909 participants with audiometric data, 12,834 with Berlin Questionnaire data, and 4,024 participants with PSG data. Those with high and very high OSA risk showed significantly elevated odds of HL at speech frequencies, with adjusted odds ratios (OR) of 1.34 (95% CI: 1.14–1.58; p < 0.01) and 1.56 (95% CI: 1.30–1.88; p < 0.01), respectively, compared to those with no OSA risk. Combining very high and high risk validated category groupings for the Berlin, those individuals had 41% higher odds for HL over speech frequencies compared to those with no risk (OR = 1.41; 95% CI = 1.21–1.65; p < 0.01). Those with PSG-determined severe OSA had higher adjusted odds of HL at speech frequencies than those with no OSA; OR of 1.33 (95% CI: 1.00-1.78; p = 0.04).ConclusionsWe report a significant association between OSA and HL among emergency responders. Our results underscore a need for an analysis of the longitudinal association between OSA and HL to identify potential causality and for integrated health interventions that target both conditions in this responder population.

IOT-Based Automatic Vehicle Accident Detection System

Road accidents remain a pressing global issue, causing significant injuries, loss of life, and economic setbacks. The delayed detection of accidents and communication to emergency responders often worsens these outcomes, highlighting the need for efficient and automated solutions. . An IoT-based Automatic Vehicle Accident Detection and Alert System has been developed to solve the identified problems by integrating superior sensors and real-time communications. The system employs an accelerometer module to continuously monitor vehicle dynamics, identifying sudden deceleration or impact patterns that signify a potential accident. Once an accident is detected, a NEO-6M GPS module accurately determines the exact location, and a GSM module instantly transmits SMS alerts to predefined contacts or emergency services, reducing response times and increasing the chances of saving lives. An Arduino Nano microcontroller acts as the system's core, seamlessly processing data from all sensors and managing the communication protocols. A DC-DC buck converter ensures stable and efficient power delivery, enhancing the system’s reliability, even in adverse conditions. Compact and cost effective, this design can be easily adapted for various vehicle types, ranging from personal cars to commercial fleets. Moreover, the system lays the foundation for future enhancements, such as mobile app integration, cloud-based accident data analysis, real-time updates to stakeholders, and predictive vehicle maintenance. By bridging the gap between accident occurrence and emergency response, this innovation not only promises to save lives but also empowers stakeholders with data-driven insights to improve road safety and vehicle reliability.

Responding to a Hazardous Materials Incident in Nepal

Paper talks about hazardous materials incidents in Nepal that threaten health, safety and the environment. Rapid urbanisation and industrial growth have made chemical, biological, radiological and nuclear emergencies more likely, but there is still not a clear national plan or complete chemical inventory, leaving the health sector unprepared. Emergency responders rely on the Emergency Response Guidebook and the Wireless Information System for Emergency Responders, but many are not aware of these tools or trained to use them. The Nepalese Army has a dedicated chemical, biological, radiological and nuclear platoon, yet hospitals still lack decontamination protocols, equipment and trained staff. Coordination between agencies is weak, resources are limited and exercises are rare. It feels like the pieces do not fit together. We suggest developing national guidelines aligned with international standards, forming dedicated response teams, running regular training sessions and including chemical incident plans in hospital disaster plans to improve preparedness .

Real Time Monitoring of Forest Fire Detection and Object Detection Using Node MCU

Forest fires pose a severe threat to the ecosystems, wildlife, and human settlements, causing widespread environmental damage through their system and loss of biodiversity. Rapid detection and accurate prediction of fire spread are crucial for effective wildfire management and mitigation. This project aims to develop the function of a real-time forest fire monitoring and detection and prediction system that leverages the Internet of Things (IoT) and machine learning (ML) to enhance early fire detection, prediction, and response mechanisms. The system is built around a Node MCU microcontroller, which acts as the core processing unit, interfacing with multiple environmental sensors to detect fire-related anomalies. These sensors include a temperature sensor for detecting sudden heat surges, an MQ-series gas sensor for identifying smoke and hazardous gases, and an anemometer with a wind vane to measure wind speed and direction—crucial factors in fire spread prediction. Once a fire is detected, the system triggers an immediate response mechanism. Authorities and emergency responders receive real-time alerts via a Python- based messaging service, which includes the precise GPS coordinates of the fire location, enabling a rapid and targeted response. Additionally, a buzzer alarm is activated in nearby areas, and it can be alert the system, through their authorities providing an audible warning to facilitate quick evacuation and preventive actions. To ensure durability and reliability in harsh environmental conditions, the entire system is mounted on a custom PCB board. The integration of IoTbased real- time monitoring with ML-driven it means machine learning can be used to real monitoring the forest fire and predictive analytics enhances decision-making capabilities, providing critical insights that aid in fire containment strategies.

Real-Time Hand and Face Gesture Recognition for Emergency Response

This paper presents an innovative system for emergency hand recognition, integrated with facial recognition technology, to provide rapid access to an individual’s medical history during critical situations. The proposed solution leverages advanced biometric analysis to identify individuals via hand and facial features, ensuring quick and reliable recognition. Upon identification, the system retrieves and displays the person’s medical records, aiding emergency responders in delivering precise and timely care. This dual-modality approach enhances accuracy, reduces response time, and demonstrates significant potential in improving emergency healthcare outcomes. The system’s design, implementation, and real-world applications are explored, emphasizing its utility in healthcare emergencies.

Crowd Evacuation in Stadiums Using Fire Alarm Prediction.

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO2 levels, and particulate matter. A comparative analysis of six machine learning models-Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet-demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety.

SPSS For Disaster Prediction: A Machine Learning and Statistical Perspective

Disaster risk management is a critical area of study, as effective preparedness and response strategies can significantly reduce the impact of hazardous events. This research evaluates key disaster response parameters, including Risk Score, Preparedness Level, Response Time, Economic Impact, and Casualty Risk, using statistical analysis. By identifying correlations and patterns among promoting improvements disaster resilience and mitigation efforts. Research Significance: This study is significant as it provides an evidence-based method to disaster preparedness and response evaluation. By analyzing key risk factors and preparedness indicators, the findings help policymakers, emergency responders, and infrastructure planners develop more effective risk reduction strategies. The research also highlights how different environmental and infrastructural conditions influence response efficiency. Methodology: The research employs SPSS statistical analysis, utilizing methods such as, regression analysis, and ANOVA—these methods help determine the relationships between evaluation parameters and assess their collective impact on disaster response effectiveness. Alternative Influencing Factors: In addition to the primary evaluation parameters, the study considers several alternative influencing factors that may impact disaster preparedness and response, including: Disaster Type (e.g., natural vs. man-made disasters) Location Type (urban vs. rural areas) Weather Conditions (floods, storms, droughts, etc.) Ground Stability (earthquake-prone areas, landslide risks) Warning System Availability (early warning mechanisms, public alert systems) Infrastructure Readiness (emergency shelters, transportation networks, healthcare facilities) Evaluation Parameters: The key parameters assessed in this study include: Risk Score: Measures the overall level of risk associated with a disaster event. Preparedness Level: Evaluates the extent of readiness before a disaster occurs. Response Time: Assesses the efficiency of emergency response efforts. Economic Impact: Estimates the financial and infrastructural losses caused by disasters. Casualty Risk: Analyzes the potential for injuries or fatalities due to a disaster. Results: The analysis reveals a strong correlation among preparedness indicators, with Principal Component Analysis (PCA) showing that over 91% of the total variance is explained by a single component. This suggests that disaster response factors are highly interdependent. Regression and ANOVA results further confirm the statistical significance of these parameters in predicting disaster outcomes. The study emphasizes the need for enhanced early warning systems, robust infrastructure, and efficient response mechanisms to mitigate disaster risks.

Measurement and Evaluation of Dust Concentrations in the Air After the Kahramanmaraş Earthquake in Turkey

The 6 February 2023 earthquake in Kahramanmaraş, Turkey, caused significant debris accumulation, raising concerns about air quality and public health. This study assessed dust concentrations during debris removal and emergency response efforts over a five-day period. Post-disaster respirable and total dust concentrations were measured using dust monitoring devices and the MDHS-14/3 gravimetric method. Scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) analyses identified fibrous structures and elements associated with asbestos, suggesting potential long-term health risks such as asbestosis and lung cancer. The average respirable dust concentration was 30.84 mg/m3, and the total dust concentration was 33.66 mg/m3. The findings emphasize the urgent need for protective measures to mitigate exposure risks for affected populations and emergency responders. Integrating health risk assessments into disaster management strategies are crucial to reducing long-term public health impacts.

The Future of Deadly Synthetic Opioids: Nitazenes and Their International Control

ABSTRACTNitazenes, a class of highly potent illicit synthetic opioids, represent an escalating global public health threat due to their increasing consumption and rising occurrence of overdose mortality connected with their use. This study evaluates the dangers posed by nitazenes, details gaps in their current international and national regulatory and enforcement measures, and proposes solutions to diminish their impact. Focusing on China and India, the two states most linked with nitazene production, the United States, the primary consumer market, and the United Nations, this research details the challenges involved in controlling these substances. Central issues include the pace of the emergence of new analogs, regulatory inconsistencies across jurisdictions, and the limited capabilities in toxicological testing. Proposed strategies for improved control include compound‐wide bans, unifying national laws with international standards, and enhanced toxicology testing capabilities for emergency responders and forensic laboratories. These findings stress the need for an adaptive and coordinated response to meet the evolving nitazene threat, with implications for public health, addiction research, and international regulatory systems.

Integrating patient metadata and pathogen genomic data: advancing pandemic preparedness with a multi-parametric simulator

Stakeholder training is essential for handling unexpected crises swiftly, safely, and effectively. Functional and tabletop exercises simulate potential public health crises using complex scenarios with realistic data. These scenarios are designed by integrating datasets that represent populations exposed to a pandemic pathogen, combining pathogen genomic data generated through high-throughput sequencing (HTS) together with patient epidemiological, clinical, and demographic information. However, data sharing between EU member states faces challenges due to disparities in data collection practices, standardisation, legal frameworks, privacy, security regulations, and resource allocation. In the Horizon 2020 PANDEM-2 project, we developed a multi-parametric training tool that links pathogen genomic data and metadata, enabling training managers to enhance datasets and customise scenarios for more accurate simulations. The tool is available as an R package: https://github.com/maous1/Pandem2simulator and as a Shiny application: https://uclouvain-ctma.Shinyapps.io/Multi-parametricSimulator/, facilitating rapid scenario simulations. A structured training procedure, complete with video tutorials and exercises, was shown to be effective and user-friendly during a training session with twenty PANDEM-2 participants. In conclusion, this tool enhances training for pandemics and public health crises preparedness by integrating complex pathogen genomic data and patient contextual metadata into training simulations. The increased realism of these scenarios significantly improves emergency responder readiness, regardless of the biological incident’s nature, whether natural, accidental, or intentional.

Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework

Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters.

Enhancing Flood Disaster Response Through Real-Time Monitoring and IoT: The Case of SentryLeaf

Floods are among the greatest natural disasters, causing immense destruction, particularly in flood-prone regions like Bangladesh. This study introduces SentryLeaf, an innovative IoT-based network for real-time flood monitoring and disaster response. The system integrates water-level sensors, environmental sensors, and communication modules to facilitate continuous monitoring, enabling quick identification of high-risk areas. The major findings of this research include the system&amp;apos;s high accuracy in data collection, with water-level sensors providing measurements accurate to ±2 cm under ideal conditions. Additionally, SentryLeaf ensures real-time data transmission and reliable communication even in the absence of traditional networks, thanks to its decentralized architecture. The communication network remained stable over distances of 200 meters, despite obstructions, and the peer-to-peer communication protocol exhibited resilience under harsh conditions. Furthermore, the system’s user interface received positive feedback for its intuitive design and responsiveness, allowing emergency responders to make informed decisions quickly. Overall, SentryLeaf significantly enhances Bangladesh’s disaster preparedness and response capabilities, offering a scalable, cost-effective, and resilient solution for mitigating flood-related damages.

Predictive Relief Logistics Models for Earthquakes and Floods Based on Traffic, Weather, and Supply Chain Data

Abstract: This research presents a predictive logistics framework designed to enhance disaster response efficiency during earthquakes and floods by leveraging integrated traffic, weather, and supply chain data. The proposed system combines deep learning models—such as LSTMs and CNNs—for disaster impact forecasting, with dynamic graph models for traffic disruption prediction and probabilistic approaches for supply chain risk assessment. A multi-objective optimization engine, powered by genetic algorithms and reinforcement learning, dynamically allocates resources, plans routes, and prioritizes delivery based on evolving disaster scenarios. Real-time data from GPS networks, meteorological sources, and inventory systems are processed and visualized via an interactive GIS-enabled dashboard. The system demonstrates significant improvements in delivery time, coverage ratio, and operational resilience compared to static models. Designed for scalability and modularity, the framework offers a powerful decision-support tool for governments, NGOs, and emergency responders aiming to minimize humanitarian impact and maximize logistical effectiveness during compound natural disasters. Keywords: disaster logistics, predictive modeling, earthquake response, flood response, traffic disruption, weather forecasting, supply chain resilience, LSTM, reinforcement learning, multi-objective optimization, humanitarian logistics, GIS, emergency response planning, dynamic routing, real-time decision support

Comparative Analysis of Mental Health Challenges and Shift Work Impact on Urban vs. Rural Emergency Responders

Purpose: This study examines the mental health challenges faced by emergency responders in urban and rural settings, analyzing the impact of shift work and organizational support on their psychological well-being. It aims to identify key differences in mental health outcomes between urban and rural responders and provide evidence-based recommendations for improving mental health support systems. Materials and Methods: This study employs a comprehensive literature review and data analysis to compare mental health challenges across geographic locations. Statistical comparisons highlight differences in the prevalence of PTSD, depression, anxiety, burnout, and sleep disorders among urban and rural responders. Visual data representations, including tables and figures, illustrate key findings to enhance understanding. Findings: The results indicate that urban emergency responders experience higher rates of PTSD (38%), depression (42%), anxiety (45%), burnout (50%), and sleep disorders (55%) compared to their rural counterparts. Shift work significantly exacerbates these mental health issues, with urban responders reporting higher levels of fatigue, stress, and overall psychological distress. Rural responders, while facing lower exposure to violent incidents, experience unique stressors such as professional isolation and limited access to mental health services. Unique Contribution to Theory, Practice and Policy: This study contributes to existing literature by highlighting the geographic disparities in mental health outcomes among emergency responders and emphasizing the role of shift work and organizational support in shaping these outcomes. In practice, the findings underscore the need for tailored interventions, such as peer support programs and targeted mental health training, to address the specific needs of urban and rural responders.

Exploring the role of virtual reality in preparing emergency responders for mass casualty incidents

BackgroundThe increasing complexity of mass casualty incidents (MCIs) necessitates highly effective training for emergency responders. Traditional training methods, while effective in teaching core skills, often fail to replicate the dynamic, high-pressure environments responders face in real-world crises. Virtual reality (VR) offers a novel approach to emergency training, providing an immersive, controlled setting that can simulate real-life scenarios. This study explores the effectiveness of VR in training paramedic students for MCIs and compares the outcomes to those from conventional training methods.MethodsA comparative study was conducted with 37 paramedic students who underwent either VR-based training or conventional training using mannequins and real-world equipment. The VR application simulated a mass casualty car accident, focusing on triage and patient management. Both groups were assessed based on their performance in key areas, including the accuracy of situational reporting (METHANE), patient triage, heart rate monitoring, and perceived demand using the NASA Task Load Index (NASA-TLX).ResultsThe VR group demonstrated significantly lower mental demand (p < 0.001) and frustration levels (p = 0.021) compared to traditional training. However, task completion times were slower in the VR setting (p < 0.001), likely due to the interface's unfamiliarity. Accuracy in situational reporting was higher in VR (p = 0.002), while heart rate monitoring did not reveal a significant difference between the groups (p = 0.516). Although VR did not reduce temporal demand (p = 0.057), it showed potential for improving focus and precision in training. Error rates in triage were similar across both training methods (p = 0.882), indicating comparable performance levels in patient classification.ConclusionsVR presents a promising tool for training emergency responders, particularly in situations that require rapid upskilling, such as crises or wars. The ability to simulate realistic, high-pressure scenarios in a controlled environment can enhance both cognitive and emotional preparedness. Further research is necessary to optimize VR systems and interfaces, making them more efficient for real-time decision-making. As VR technology advances, it holds potential as a key component in future emergency preparedness strategies.