A. R. Ruis

Using epistemic network analysis to identify targets for educational interventions in trauma team communication

Background. Epistemic Network Analysis (ENA) is a technique for modeling and comparing the structure of connections between elements in coded data. We hypothesized that connections among team discourse elements as modeled by ENA would predict the quality of team performance in trauma simulation. Methods. The Modified Non‐technical Skills Scale for Trauma (T‐NOTECHS) was used to score a simulation‐based trauma team resuscitation. Sixteen teams of 5 trainees participated. Dialogue was coded using Verbal Response Modes (VRM), a speech classification system. ENA was used to model the connections between VRM codes. ENA models of teams with lesser T‐NOTECHS scores (n = 9, mean = 16.98, standard deviation [SD] = 1.45) were compared with models of teams with greater T‐NOTECHS scores (n = 7, mean = 21.02, SD = 1.09). Results. Teams had different patterns of connections among VRM speech form codes with regard to connections among questions and edifications (meanHIGH = 0.115, meanLOW = −0.089; t = 2.21; P = .046, Cohen d = 1.021). Greater‐scoring groups had stronger connections between stating information and providing acknowledgments, confirmation, or advising. Lesser‐scoring groups had a stronger connection between asking questions and stating information. Discourse data suggest that this pattern reflected increased uncertainty. Lesser‐scoring groups also had stronger connections from edifications to disclosures (revealing thoughts, feelings, and intentions) and interpretations (explaining, judging, and evaluating the behavior of others). Conclusion. ENA is a novel and valid method to assess communication among trauma teams. Differences in communication among higher‐ and lower‐performing teams appear to result from the ways teams use questions. ENA allowed us to identify targets for improvement related to the use of questions and stating information by team members.

A network analytic approach to gaze coordination during a collaborative task

Abstract A critical component of collaborative learning is the establishment of intersubjectivity, or the construction of mutual understanding. Collaborators coordinate their understanding with one another across various modes of communication, including speech, gesture, posture, and gaze. Given the dynamic, interdependent, and complex nature of coordination, this study sought to develop and test a method for constructing detailed and nuanced models of coordinated referential gaze patterns. In the study, 13 dyads participated in a simple collaborative task. We used dual mobile eye tracking to record each participants gaze behavior, and we used epistemic network analysis (ENA) to model the gazes of both conversational participants synchronously. In the model, the nodes in the network represent gaze targets for each participant, and the connections between nodes indicate the likelihood of gaze coordination. Our analyses indicate: (a) properties and patterns of how gaze coordination unfolds throughout an interaction sequence; and (b) differences in gaze coordination patterns for interaction sequences that lead to breakdowns and repairs. In addition to contributing to the growing body of knowledge on the coordination of gaze behaviors in collaborative activities, this work suggests that ENA enables more effective modeling of gaze coordination.

Annals and Analytics: The Practice of History in the Age of Big Data

As much as historians would like to add a time machine to their toolkit, research in history is not problematic only because of insufficient data, nor because most extant data have passed through the filtration and interpretation of second-hand observation. On the contrary, for many historians the sheer quantity of available information – what William Turkel terms the ‘infinite archive’ of digital materials – cannot be processed using traditional methods alone. Far from solving this problem, a time machine would exacerbate it, adding more and richer data into the mix. In addition, there are important historical questions that cannot be answered solely through close readings of texts or through direct observation of the past; both cases overestimate the historian’s powers of observation, implying that critical analysis and ethnography can solve all historical puzzles. Yet it is also dangerous to assume that more or more accurate data will necessarily lead to better understanding. The view that computers can take massive amounts of information and do most of our analytic thinking for us, a belief embraced by many data miners and glorified by tech evangelists, often yields statistically significant but conceptually meaningless results. We can and should outsource some of our thinking to smart machines, much as we have outsourced some of our memory to books and other media; but to do this well is to understand the limitations and leverage the affordances of different approaches to processing and analysing information, both human and machine. The practice of historical research stands to benefit from, and may even require, a mixed-methods approach that incorporates the analytic strengths of human interpretation and computational processing. In this brief reflection, I explore one approach to mixedmethods history using network analysis: various statistical techniques with which the structure of connections among entities – people, places, concepts and so forth – can be modelled. Network analytic approaches are commonly applied when the connections among entities reveal more than analysis of the entities in isolation. Simply knowing which historians attended the opening reception at a conference, for example, does not contribute as much to our understanding of that scholastic community as knowing who talked to whom during the event. To the extent that historians have used network analytic techniques, most have modelled explicit historical networks: correspondence communities, shipping networks, citation patterns and so forth.2 These are models in which both the nodes (the entities connected

Teaching health care workers to adopt a systems perspective for improved control and prevention of health care–associated infections

An estimated 4% of inpatients in U.S. acute care hospitals are diagnosed with preventable health care–associated infections (HAIs).1 Although concerted infection control efforts have achieved reductions in some HAIs,2 the prevalence of HAIs as a whole is growing. This rise in prevalence is occurring despite increasing efforts to improve infection control protocols and implement prevention measures.3 Research4,5 has shown that improvements in infection control and prevention are dependent not only on training health care workers (HCWs) to perform clinical techniques and apply administrative protocols but also on (1) the extent to which such practices are accepted by HCWs as useful or necessary and (2) the identification and removal of barriers to implementation. We argue that procedural approaches alone, even with high levels of adherence, are often insufficient to solve the growing problem of HAIs; it is equally important that interventions address themore complex cognitive aspects of HAI control and prevention. HCWs facemany patient care situations for which standard procedures have not been and cannot be developed. In these cases, HCWs must make decisions with incomplete information and a high degree of uncertainty; understand and balance risk versus reward; account for numerous demands, including those of patients, hospital administrators, and insurance providers; coordinate care across multiple contexts and caregivers; and exercise clinical judgment. To implement a reduction in HAIs therefore requires that HCWs follow infection control procedures but also make clinical decisions related to HAI prevention, and the former must be situated within the latter. This suggests that improvements in infection control education and training for HCWs are critical for reducing HAIs. In this commentary, our aim is to characterize and discuss the affordances of a novel approach to this problem based on cognitive simulation6-8: a practice-based intervention inwhich participants learn to solve multidimensional problems characterized by incomplete information, multiple stakeholders, and incommensurate demands. This approach, though as yet untried in the context of the complex arena of HAI prevention, has the potential to improve implementation of and adherence to infection control procedures and, more importantly, help HCWs learn to make clinical decisions that promote HAI control and prevention. There are, of course, many types of cognitive simulation in medical education,6,9-12 and they are used for a wide range of training purposes. According to a recent review by Satish et al,13 however, most cognitive simulations seek to replicate the challenges and demands of clinical practice, providing a safe setting for HCWs to rehearse skills, gain confidence, and develop their abilities in case management, critical thinking, decision-making, and other important aspects of professional practice. However, simulating day-today practice does not help HCWs understand the systemic aspects of the problem and the bigger picture of HAI prevention. * Address correspondence to Nasia Safdar, MD, PhD, Department of Medicine, Division of Infectious Disease, School of Medicine and Public Health, University of Wisconsin–Madison, 5138 Centennial Building, 1685 Highland Avenue, Madison, WI 53705. E-mail address: ns2@medicine.wisc.edu (N. Safdar). Funding/support: This work was funded in part by the National Science Foundation (DRL-0918409, DRL-0946372, DRL-1247262, DRL-1418288, DUE-0919347, DUE1225885, EEC-1232656, EEC-1340402, and REC-0347000), theMacArthur Foundation, the Spencer Foundation, the Wisconsin Alumni Research Foundation, and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin–Madison. N.S. is supported by a Veterans Affairs–funded patient safety center and an R03 from AHRQ. Conflicts of interest: None to report. Disclaimer: The opinions, findings, and conclusions do not reflect the views of the funding agencies, cooperating institutions, or other individuals.