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Edwards GF, Mierisch C, Mutcheson B, Strauss A, Coleman K, Horn K, Parker SH. Developing medical simulations for opioid overdose response training: A qualitative analysis of narratives from responders to overdoses. PLoS One 2024; 19:e0294626. [PMID: 38547079 PMCID: PMC10977769 DOI: 10.1371/journal.pone.0294626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/06/2023] [Indexed: 04/02/2024] Open
Abstract
Medical simulation offers a controlled environment for studying challenging clinical care situations that are difficult to observe directly. Overdose education and naloxone distribution (OEND) programs aim to train potential rescuers in responding to opioid overdoses, but assessing rescuer performance in real-life situations before emergency medical services arrive is exceedingly complex. There is an opportunity to incorporate individuals with firsthand experience in treating out-of-hospital overdoses into the development of simulation scenarios. Realistic overdose simulations could provide OEND programs with valuable tools to effectively teach hands-on skills and support context-sensitive training regimens. In this research, semi-structured interviews were conducted with 17 individuals experienced in responding to opioid overdoses including emergency department physicians, first responders, OEND program instructors, and peer recovery specialists. Two coders conducted qualitative content analysis using open and axial thematic coding to identify nuances associated with illicit and prescription opioid overdoses. The results are presented as narrative findings complemented by summaries of the frequency of themes across the interviews. Over 20 hours of audio recording were transcribed verbatim and then coded. During the open and axial thematic coding process several primary themes, along with subthemes, were identified, highlighting the distinctions between illicit and prescription opioid overdoses. Distinct contextual details, such as locations, clinical presentations, the environment surrounding the patient, and bystanders' behavior, were used to create four example simulations of out-of-hospital overdoses. The narrative findings in this qualitative study offer context-sensitive information for developing out-of-hospital overdose scenarios applicable to simulation training. These insights can serve as a valuable resource, aiding instructors and researchers in systematically creating evidence-based scenarios for both training and research purposes.
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Affiliation(s)
- G. Franklin Edwards
- Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Cassandra Mierisch
- Department of Basic Science Education, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, United States of America
| | - Brock Mutcheson
- Office of Assessment and Program Evaluation, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, United States of America
| | - Allison Strauss
- Virginia Tech Carilion School of Medicine, Roanoke, Virginia, United States of America
| | - Keel Coleman
- Department of Emergency Medicine, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, United States of America
- Department of Emergency Medicine, Carilion Clinic, Roanoke, Virginia, United States of America
| | - Kimberly Horn
- Department of Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, Virginia, United States of America
- Fralin Biomedical Research Institute at VTC, Roanoke, Virginia, United States of America
| | - Sarah Henrickson Parker
- Fralin Biomedical Research Institute at VTC, Roanoke, Virginia, United States of America
- Department of Health Systems and Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, United States of America
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