Capturing Networked Knowledge in a Doctoral Research Training Network: First Findings—The MINDSHIFT Case Study

ABSTRACT

MINDSHIFT was an EU‐funded doctoral research‐training network on hypertension involving 15 PhD projects across six universities. We present MINDSHIFT as a case study of how a distributed network may develop, structure, and integrate mechanistic knowledge across diverse projects. Our specific aim is to translate the mechanistic details of each project into a shared descriptive framework using the black box modelling terms input, process, and output. We used an iterative qualitative study design with 3 cycles combining conceptual development and training activities, including interviews and workshops. This process yielded project‐specific black box representations and 86 candidate cross‐project connections generated by 12 project teams. The findings suggest that the approach supported structured comparison, articulation of mechanistic relations, and reflective exploration across projects within the research network setting.