RESUMEN
ABSTRACT: Hazardous Materials (HAZMAT) Technicians' notions of mental model, or cognitive representations of their understanding and beliefs regarding Radiological Dispersal Devices (RDDs) incidents, have not been previously explored. A prior study developed an Expected Mental Model State (EMMS) framework specific to RDD incident response for HAZMAT technicians. The work herein presents the development of a derivative of this framework, the EMMS Diagnostic Matrix, to evaluate the actual Mental Model State (MMS) of HAZMAT technicians in the context of RDD incidents. The EMMS Diagnostic Matrix was administered via a survey and simulation activity in four U.S. states representing the Northeast, West, South, and Midwest regions. Data were collected and coded using grounded theory methodology. Reflexive thematic analysis was employed to identify themes across related areas where the notions of mental model for the HAZMAT technician responders' actual MMS differed from the EMMS. The analysis of the collected data revealed four significant themes representing incomplete notions of the mental model spanning various EMMS conceptual domains: Overestimation of Radiation Dose and Health Effects, indicating misunderstandings about the health impacts of radiation exposure, Acute Radiation Syndrome (ARS), particularly in the lower range of radiation doses; Overreliance on Responder Protection [personal protective equipment (PPE)/self-contained breathing apparatus (SCBA)], highlighting gaps in understanding radiation principles and radioactive material dispersal properties from a radiological dispersal device; Misunderstanding Radiation Detection and Units, signifying confusion about radiation units and differentiation between dose rate and accumulated dose; and Incomplete Understanding of Radiation Characteristics and Dispersal Properties, outlining a limited grasp of inhalation risks from radiation and the dispersal traits of a radiological dispersal device. The interconnectedness of these technical misunderstandings can guide the development of a strategic plan to evaluate and modify existing training, aiming at these specific themes to improve the efficiency of HAZMAT technicians in emergency situations and to identify areas for further research.
RESUMEN
ABSTRACT: This research examines the cognitive frameworks used by HAZMAT technicians when responding to incidents involving Radiological Dispersal Devices (RDDs), which are conventional explosive devices with radioactive materials incorporated. The objective is to introduce the Expected Mental Model State (EMMS) as a comprehensive evaluation tool for assessing and enhancing the expertise and situational awareness of emergency responders dealing with radiation crises. Through a series of expert focus group sessions using the well-established qualitative methodology of grounded theory, an Expected Mental Model State (EMMS) was developed. The methodology used an influence diagram architecture to conceptually capture and codify key areas relevant to effective emergency response. The research identifies fourteen EMMS key conceptual domains, further elaborated into 301 subtopics, providing a multi-dimensional structure for the proposed mental model framework. Three pivotal notions of mental model emerged within the EMMS framework: Knowledge Topology, Envisioning (Belief), and Response and Operability. These notions were found to align with previous theories of mental models and are vital for understanding how HAZMAT technicians conceptualize and respond to RDD incidents. The study emphasizes the critical role of mental models in enhancing preparedness and effective response strategies during radiation emergencies. The EMMS framework offers a versatile methodology that can be adapted across various kinds of emergency responders and high-risk situations, including the broader Chemical, Biological, Radiological, and Nuclear (CBRN) spectrum. Using this EMMS framework to develop an EMMS Diagnostic Matrix can provide a roadmap for identifying areas for the development of specialized training modules that have the potential to significantly elevate both the quality and efficacy of responder training and preparation.