The hidden factor: Characterizing the influence of responder mental models on the quality of radiological dispersal device incident response

dc.contributor.advisor Keren, Nir
dc.contributor.advisor Mosher, Gretchen
dc.contributor.advisor Franke, Warren
dc.contributor.advisor Shelley, Mack
dc.contributor.advisor Simpson, Stephen
dc.contributor.author Leek, Angela E
dc.contributor.department Agricultural and Biosystems Engineering en_US
dc.date.accessioned 2024-01-25T20:20:45Z
dc.date.available 2024-01-25T20:20:45Z
dc.date.issued 2023-12
dc.date.updated 2024-01-25T20:20:45Z
dc.description.abstract This research examines the cognitive frameworks, namely mental models, utilized by HAZMAT technicians when responding to incidents involving Radiological Dispersal Devices (RDDs). RDDs are conventional explosive devices with radioactive materials incorporated. Responders train to respond to radiation hazards during incidents, yet these incidents present distinct situational awareness challenges compared to other response scenarios due to the low probability but high impact nature of radiological incidents. The objective of this research is to develop a comprehensive evaluation methodology for assessing and weighting the impact of responders’ mental model state related to radiation and risk on their response quality. It is anecdotally known that radiation and radiation emergency response concepts are challenging for responders, and other professions, who are not engaged in them routinely. An empirical exploration into which specific concepts are most challenging and have the potential to impact response quality has not been previously done. The work herein aims to provide a mechanism to explore this with the ultimate goal of enhancing the expertise and proficiency of responders. By understanding where the mental models are incomplete, policies, doctrine, and strategic approaches for preparing emergency responders can be adapted and created to refine and enhance responders’ preparedness and capacity to directly improve the quality of their responses to such emergencies. The first phase of this research utilized an expert focus group to develop an Expected Mental Model State (EMMS) for HAZMAT technicians in RDD incidents. The methodology used the well-established qualitative grounded theory methodology to create an influence diagram architecture to conceptually capture and codify key areas relevant to effective emergency response. The derived EMMS established the following fourteen key conceptual principle domains: (1) radiation detection equipment, (2) radiation detection operations, (3) response zones, (4) worker protective considerations, (5) public protective considerations, (6) important reference documents, (7) responder confidence level/trust, (8) inherent radiation sentiment origins, (9) core understandings about RDD response, (10) radiation protection principles, (11) radiation dose, (12) radiation characteristics, (13) realistic RDD principles of dispersion of material, and (14) radiation units. Figure 1 presents a collapsed overview of the EMMS structure, reflecting the conceptual domains; from which 301 specific subtopics emerged. These conceptual domains related to three notions of mental model: (1) knowledge topology notion, (2) envisioning notion, and (3) response and operability notion. In the second phase, these domains and notions were used to develop an EMMS Diagnostic Matrix. This Matrix serves as a framework for assessing current mental model states of responders in relation to the EMMS. The EMMS Diagnostic Matrix was employed to collect data from HAZMAT technician-level first responders in four cities across the United States, aiming to elicit individual responder mental model states (MMS). It was utilized to map the elicited responder MMS against the EMMS, identifying gaps in understanding or incomplete mental models. In the final phase, responders were assigned a quality score based on their handling of a simulated RDD incident in a virtual reality environment. Thirteen theme elements, indicative of incomplete notions in the MMS, were extracted. The impact of these themes on response quality was then evaluated. Results showed that six of these theme elements had a statistically significant effect on response quality. These findings are valuable for prioritizing future targeted training efforts to effectively bridge these gaps and enhance responder preparedness. The study emphasizes the critical role of mental models in enhancing preparedness and effective response strategies during radiation emergencies. The EMMS Diagnostic Matrix 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 or workers in hazardous occupational settings. By establishing this methodology, future work can use this process to assess an individual’s mental model using the EMMS Diagnostic Matrix, along with virtual reality simulations to assess the quality of responder or worker actions. This framework provides a roadmap for identifying conceptual areas where specialized training modules should be offered or developed. Focusing existing responder training and developing new experiential training that can complete an individual’s mental model regarding radiation and risk opens the potential to significantly elevate both the quality and efficacy of training and the preparedness of responders and workers managing hazardous situations.
dc.format.mimetype PDF
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/dv6lVbbz
dc.language.iso en
dc.language.rfc3066 en
dc.subject.disciplines Nuclear physics and radiation en_US
dc.subject.disciplines Cognitive psychology en_US
dc.subject.keywords emergency preparedness en_US
dc.subject.keywords emergency response en_US
dc.subject.keywords HAZMAT en_US
dc.subject.keywords radiation en_US
dc.subject.keywords safety en_US
dc.title The hidden factor: Characterizing the influence of responder mental models on the quality of radiological dispersal device incident response
dc.type article en_US
dc.type.genre dissertation en_US
dspace.entity.type Publication
thesis.degree.discipline Nuclear physics and radiation en_US
thesis.degree.discipline Cognitive psychology en_US
thesis.degree.grantor Iowa State University en_US
thesis.degree.level dissertation $
thesis.degree.name Doctor of Philosophy en_US
File
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Leek_iastate_0097E_21285.pdf
Size:
2.82 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
0 B
Format:
Item-specific license agreed upon to submission
Description: