Team reflection makes resilience-related knowledge explicit through collaborative sensemaking: observation study at a rail post

Resilience is defined as the ability to adaptively deal with system boundaries in the face of the unexpected and unforeseen (Branlat and Woods in AAAI Fall Sympoisum, 2010. http://www.aaai.org/ocs/index.php/FSS/FSS10/paper/viewPaper/2238). We hypothesize that drawing upon resilience-related knowledge is a prerequisite for such adaptivity. This paper proposes team reflection (Ellis et al. in Curr Dir Psychol Sci 23(1):67–72, 2014) as a macrocognitive function to make the resilience-related knowledge explicit. This knowledge is implicitly available with individual team members active at the sharp end but is never explicitly shared due to invisibility of goal-relevant constraints. To overcome this invisibility, we suggest an application that makes changes in the current rail socio-technical system visible in terms of the three system boundaries, a variation of the originally proposed by Rasmussen (Saf Sci 27(2/3):183–213, 1997): safety, performance and workload. This allows a team of rail signallers to analyse movements towards system boundaries and share knowledge on these movements. An observational study at a rail control post was conducted to assess the value of team reflection in making resilience-related knowledge explicit. For this purpose, we developed a first prototype of the application concerning the performance boundary only. Using naturalistic observations of a team during a week, we observed how they reflected at the end of their shift on salient system changes. A global content analysis was used to show the relevance of the content to resilience and to test the increase in the resilience-related knowledge throughout the observation period. A specific case of a human approaching the rail tracks, as a potential suicide, was analysed in detail. The results show the value of team reflection on system movements towards their boundaries, thus making goal-relevant constrained knowledge explicit within the operational rail environment.