Integrated knowledge translation meets complexity science

This chapter explores how integrated knowledge translation and complexity science intersect to inform more adaptive, relational, and systems-responsive approaches to research translation in healthcare. It begins by following the evolution of the field of knowledge translation from linear models toward collaborative, iterative, and context-sensitive processes. A range of integrated knowledge translation theories, models, and frameworks are introduced, followed by an exploration of how complexity science can enrich these approaches by acknowledging non-linearity, emergence, and the dynamic nature of systems. The chapter presents the Knowledge Translation Complexity Network Model as a synthesis of integrated knowledge translation and complexity thinking, offering a structured yet flexible guide for researchers navigating interconnected research processes including problem identification, knowledge creation, knowledge synthesis, implementation, and evaluation. Guiding principles such as contextual tailoring, transdisciplinary collaboration, and adaptive learning are discussed alongside concepts from network science, including nodes, hubs, clusters, and networks. The chapter prepares readers to approach knowledge translation as a dynamic, co-created, and system-aware activity.