The Functional Resonance Analysis Method as a Debriefing Tool in Scenario-Based-Training

The aim of this study is to discuss the use of the Functional Resonance Analysis Method (FRAM) as a debriefing tool in Scenario-Based-Training (SBT). This discussion is based on data collected during a training simulation session carried out as part of a Research and Development Project involving the development of resilience skills of grid electricians. The scenario of this simulation had a client complaining that the power had went off in his residence. The participants of the debriefing identified seven functions performed by the trainees. The use of the FRAM pointed out that there was variability in the outputs of two functions: and . Concerning the function the work constraints, such as time pressure, encouraged workers to make a temporary repair, rather than replacing the cable for a new one. In the debriefing, two actions to re-design the work system were raised: to increase investments in preventive maintenance; and to improve the design of lifting equipment and tools. The instantiation presented showed that using FRAM models and concepts (e.g. output variability, couplings, and functions) can be useful for analyzing workers’ and system’s performance in the debriefing, since it presents the resonance arising from the variability of everyday performance and lead to recommendations for coping with the variability.

[1]  Scott I. Tannenbaum,et al.  Do Team and Individual Debriefs Enhance Performance? A Meta-Analysis , 2013, Hum. Factors.

[2]  Guillaume Alinier,et al.  Determining the value of simulation in nurse education: study design and initial results. , 2004, Nurse education in practice.

[3]  E. Hollnagel,et al.  Where the rubber meets the road: using FRAM to align work-as-imagined with work-as-done when implementing clinical guidelines , 2015, Implementation Science.

[4]  M. Hazinski,et al.  Education in resuscitation. , 2003, Resuscitation.

[5]  J. Cannon-Bowers,et al.  Optimizing learning in surgical simulations: guidelines from the science of learning and human performance. , 2010, The Surgical clinics of North America.

[6]  Anne Lippert,et al.  Development of a formative assessment tool for measurement of performance in multi-professional resuscitation teams. , 2010, Resuscitation.

[7]  E. Hollnagel.  FRAM: The Functional Resonance Analysis Method: Modelling Complex Socio-technical Systems , 2012 .

[8]  J. Paige.  Surgical team training: promoting high reliability with nontechnical skills. , 2010, The Surgical clinics of North America.

[9]  Eduardo Salas,et al.  Why Training Team Decision Making is Not as Easy as You Think: Guiding Principles and Needs , 2017 .

[10]  Tarcisio Abreu Saurin,et al.  The design of scenario-based training from the resilience engineering perspective: a study with grid electricians. , 2014, Accident; analysis and prevention.

[11]  Thomas J. Chermack,et al.  Using Scenarios to Develop Crisis Managers: Applications of Scenario Planning and Scenario-Based Training , 2008 .

[12]  David M. Gaba,et al.  Simulation-Based Training in Anesthesia Crisis Resource Management (ACRM): A Decade of Experience , 2001 .

[13]  Erik Hollnagel,et al.  Resilience Engineering in Practice: A Guidebook , 2012 .

[14]  David A Cook,et al.  Teaching first or teaching last: does the timing matter in simulation-based surgical scenarios? , 2010, Journal of surgical education.