Resilience as a Concept for Convergence Across Health, Systems, and Well-Being: An AI-Augmented Mapping of 50 Years of Resilience Research

Resilience has become a focal point of academic research investigating the impact of adverse disruption to the well-being of people, systems, the built environment, ecosystems, and climate. However, the proliferation of this work has not been accompanied by increasing clarity about the core meaning of resilience as a singular construct, threatening its relevance and complicating its use in practice. To improve the application of resilience in cross-disciplinary and convergence approaches to sustainability and well-being research, this work synthesized resilience conceptualizations across disciplines with novel artificial intelligence (AI)-augmented approaches. Using open-source applications for text mining and machine-learning-based natural language processing algorithms for the examination of text-as-data, this work mapped the content of 50 years of academic resilience work (24,732 abstracts). Presented as thematic and statistical textual associations in a series of network maps and tables, the findings highlight how specific measurements, components, and terminologies of resilience relate to one another within and across disciplines, emphasizing what concepts can be used to bridge disciplinary boundaries. From this, a converged conceptualization is derived to answer theoretical questions about the nature of resilience and define it as a dynamic process of control through the stages of disruption and progression to an improved state thereafter. This conceptualization supports a cross-disciplinary meaning of resilience that can enhance its shared understanding among a variety of stakeholders, and ultimately, the rigor and uniformity of its application in addressing sustainability and well-being challenges across multiple domains.