From communication to action: using ordered network analysis to model team performance in clinical simulation | BMC Medical Education
Kolbe M, Boos M. Laborious but elaborate: the benefits of really studying team dynamics. Front Psychol. 2019;28:10.
Guise JM, Deering SH, Kanki BG, Osterweil P, Li H, Mori M, et al. Validation of a tool to measure and promote clinical teamwork. Simul Healthc. 2008;3(4):217–23.
Stevenson C, Bhangu A, Jung JJ, MacDonald A, Nolan B. The development and measurement properties of the trauma NOn-TECHnical skills (T-NOTECHS) scale: a scoping review. Am J Surg. 2022;224(4):1115–25.
Cooper S, Cant R, Porter J, Sellick K, Somers G, Kinsman L, et al. Rating medical emergency teamwork performance: Development of the Team Emergency Assessment Measure (TEAM). Resuscitation. 2010;81(4):446–52.
Hughes AM, Gregory ME, Joseph DL, Sonesh SC, Marlow SL, Lacerenza CN, et al. Saving lives: a meta-analysis of team training in healthcare. J Appl Psychol. 2016;101(9):1266–304.
Popov V, Mateju N, Jeske C, Lewis KO. Metaverse-based simulation: a scoping review of charting medical education over the last two decades in the lens of the ‘marvelous medical education machine.’ Ann Med. 2024;56(1):2424450.
Rochlen LR, Putnam EM, Tait AR, Du H, Popov V. Sequential behavioral analysis: a novel approach to help understand clinical decision-making patterns in extended reality simulated scenarios. Simul Healthc. 2023;18(5):321–5.
Klonek F, Gerpott FH, Lehmann-Willenbrock N, Parker SK. Time to go wild: How to conceptualize and measure process dynamics in real teams with high-resolution. Organ Psychol Rev. 2019;9(4):245–75.
van Eijndhoven KHJ, Wiltshire TJ, Hałgas EA, Gevers JMP. A computational approach to examining team coordination breakdowns during crisis situations. J Cogn Eng Decis Mak. 2023;17(3):256–78.
Xing W, Zhu G, Arslan O, Shim J, Popov V. Using learning analytics to explore the multifaceted engagement in collaborative learning. J Comput High Educ. 2023;35(3):633–62.
Isaak RS, Stiegler MP. Review of crisis resource management (CRM) principles in the setting of intraoperative malignant hyperthermia. J Anesth. 2016;30(2):298–306.
Quick J, Murthy R, Goyal N, Margolis S, Pond G, Jenkins K. Malignant hyperthermia: an anesthesiology simulation case for early anesthesia providers. MedEdPORTAL. 2017;13:10550.
Parsons SM, Kuszajewski ML, Merritt DR, Muckler VC. High-fidelity simulation training for nurse anesthetists managing malignant hyperthermia: a quality improvement project. Clin Simul Nurs. 2019;26:72–80.
Cain CL, Riess ML, Gettrust L, Novalija J. Malignant hyperthermia crisis: optimizing patient outcomes through simulation and interdisciplinary collaboration. AORN J. 2014;99(2):300–11.
Searle JR. Speech Acts: An Essay in the Philosophy of Language. Cambridge: Cambridge University Press; 1969. https://doi.org/10.1017/CBO9781139173438.
Stiles WB. Taxonomy of verbal response modes (VRM). The sourcebook of listening research: Methodology and measures. 2017. p. 605–11.
Sullivan S, Warner-Hillard C, Eagan B, Thompson RJ, Ruis AR, Haines K, et al. Using epistemic network analysis to identify targets for educational interventions in trauma team communication. Surgery. 2018;163(4):938–43.
Ruis AR, Rosser AA, Quandt-Walle C, Nathwani JN, Shaffer DW, Pugh CM. The hands and head of a surgeon: Modeling operative competency with multimodal epistemic network analysis. Am J Surg. 2018;216(5):835–40.
Popov V, Tan Y, Manojlovich M. Applying ordered network analysis to video-recorded physician–nurse interactions to examine communication patterns associated with shared understanding in inpatient oncology care settings. BMJ Open. 2024;14(6):e084653.
Schmutz JB, Lei Z, Eppich WJ. Reflection on the Fly: Development of the Team Reflection Behavioral Observation (TuRBO) system for acute care teams. Acad Med. 2021;96(9):1337–45.
Tan Y, Ruis AR, Marquart C, Cai Z, Knowles MA, Shaffer DW. Ordered network analysis. 2023. pp. 101–16.
Zhao L, Tan Y, Gašević D, Shaffer DW, Yan L, Alfredo R, et al. Analysing verbal communication in embodied team learning using multimodal data and ordered network analysis. 2023. p. 242–54.
Shaffer DW, Collier W, Ruis AR. A tutorial on epistemic network analysis: analyzing the structure of connections in cognitive, social, and interaction data. J Learning Analytics. 2016;3(3):9–45.
Wooldridge AR, Carayon P, Shaffer DW, Eagan B. Quantifying the qualitative with epistemic network analysis: a human factors case study of task-allocation communication in a primary care team. IISE Trans Healthc Syst Eng. 2018;8(1):72–82.
Bowman D, Swiecki Z, Cai Z, Wang Y, Eagan B, Linderoth J, et al. The mathematical foundations of epistemic network analysis. 2021. pp. 91–105.
Hopkins PM, Girard T, Dalay S, Jenkins B, Thacker A, Patteril M, et al. Malignant hyperthermia 2020. Anaesthesia. 2021;76(5):655–64.
Rosenberg H, Sambuughin N, Riazi S, Dirksen R. Malignant hyperthermia susceptibility. 1993.
ELAN. Nijmegen: Max Planck Institute for Psycholinguistics: The Language Archive; 2023. Available from: Retrieved from 9 Feb 2025.
Lausberg H, Sloetjes H. Coding gestural behavior with the NEUROGES-ELAN system. Behav Res Methods. 2009;41(3):841–9.
McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276–82.
Marquart CL, Tan Y, Cai Z, Shaffer DW. ONA: Ordered Network Analysis. R package version 0.1; 2023.
Tan Y, Swiecki Z, Ruis A, Shaffer D. Epistemic network analysis and ordered network analysis in learning analytics. In: Saqr M, López-Pernas S, (eds). Learning analytics methods and tutorials: a practical guide using R. Springer, this volume, 2024. 2024. https://cran.r-project.org/web/packages/rENA/rENA.pdf.
Siebert-Evenstone AL, Arastoopour Irgens G, Collier W, Swiecki Z, Ruis AR, Williamson Shaffer D. In search of conversational grain size: modeling semantic structure using moving stanza windows. J Learn Analytics. 2017;4(3):123.
Yule S, Flin R, Maran N, Rowley D, Youngson G, Paterson-Brown S. Surgeons’ Non-technical Skills in the Operating Room: Reliability Testing of the NOTSS Behavior Rating System. World J Surg. 2008;32(4):548–56.
Lie D, May W, Richter-Lagha R, Forest C, Banzali Y, Lohenry K. Adapting the McMaster-Ottawa scale and developing behavioral anchors for assessing performance in an interprofessional team observed structured clinical encounter. Med Educ Online. 2015;20(1):26691.
Yan L, Echeverria V, Jin Y, Fernandez-Nieto G, Zhao L, Li X, et al. Evidence-based multimodal learning analytics for feedback and reflection in collaborative learning. Br J Edu Technol. 2024;55(5):1900–25.
Zhao L, Echeverria V, Tan Y, Yan L, Li X, Alfredo R, et al. Ordered networked analysis of multimodal data in healthcare simulations: dissecting team communication tactics. 2024.
Benner P, Kyriakidis PH, Stannard D. Clinical wisdom and interventions in acute and critical care: a thinking-in-action approach, 2nd ed. Clinical wisdom and interventions in acute and critical care: A thinking-in-action approach, 2nd ed. New York: Springer Publishing Company; 2011. 576, xxiii, 576–xxiii p.
Wahr JA, Prager RL, Abernathy JH, Martinez EA, Salas E, Seifert PC, et al. Patient safety in the cardiac operating room: human factors and teamwork. Circulation. 2013;128(10):1139–69.
Zhao L, Gašević D, Swiecki Z, Li Y, Lin J, Sha L, et al. Towards automated transcribing and coding of embodied teamwork communication through multimodal learning analytics. Br J Edu Technol. 2024;55(4):1673–702.
Rosser AA, Qadadha YM, Thompson RJ, Jung HS, Jung S. Measuring the impact of simulation debriefing on the practices of interprofessional trauma teams using natural language processing. Am J Surg. 2023;225(2):394–9.
Kolbe M, Grande B, Lehmann-Willenbrock N, Seelandt JC. Helping healthcare teams to debrief effectively: associations of debriefers’ actions and participants’ reflections during team debriefings. BMJ Qual Saf. 2023;32(3):160–72.
Kolbe M, Boos M. Laborious but elaborate: the benefits of really studying team dynamics. Front Psychol. 2019;28:10.
Aloini D, Ferraro G, Iovanella A, Stefanini A. Rethinking healthcare teams’ practices using network science: implications, challenges, and benefits. Appl Sci. 2022;12(12):5841.
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