: This conceptual paper examines the growing integration of Complex Adaptive Systems (CAS) theory within nursing research and highlights a critical gap between conceptual adoption and methodological implementation. While complexity science has gained increasing recognition as a framework for understanding healthcare systems, characterized by nonlinearity, emergence, and dynamic interactions, its empirical application remains limited. Current CAS-informed nursing studies are predominantly qualitative and descriptive, providing valuable theoretical insights but lacking the methodological capacity to model system dynamics, test nonlinear relationships, or generate predictive knowledge. To address this gap, the paper proposes a structured progression toward operationalizing complexity science in nursing through three interconnected methodological shifts: measurement, simulation, and intervention. First, it emphasizes the need for robust, multilevel measurement frameworks capable of capturing adaptive competence not as a set of discrete skills, but as a higher-order construct emerging from interconnected domains and contextual interactions. Second, it advocates for the integration of computational approaches, including agent-based modeling and system dynamics, to simulate healthcare processes and explore emergent system behaviors. Third, it highlights the importance of developing longitudinal and intervention-based research designs to evaluate the impact of complexity-informed strategies on outcomes such as workforce resilience, patient safety, and system performance. The Nursing Theory of CAS (NTCAS) is presented as a conceptual scaffold supporting this transition from theoretical recognition to empirical execution. The paper concludes that advancing methodological alignment with system complexity is essential for transforming complexity science in nursing from a descriptive paradigm into a predictive and actionable framework.
Beyond conceptual adoption: Advancing complexity science in nursing through measurement, simulation, and intervention
Notarnicola, Ippolito
2026-01-01
Abstract
: This conceptual paper examines the growing integration of Complex Adaptive Systems (CAS) theory within nursing research and highlights a critical gap between conceptual adoption and methodological implementation. While complexity science has gained increasing recognition as a framework for understanding healthcare systems, characterized by nonlinearity, emergence, and dynamic interactions, its empirical application remains limited. Current CAS-informed nursing studies are predominantly qualitative and descriptive, providing valuable theoretical insights but lacking the methodological capacity to model system dynamics, test nonlinear relationships, or generate predictive knowledge. To address this gap, the paper proposes a structured progression toward operationalizing complexity science in nursing through three interconnected methodological shifts: measurement, simulation, and intervention. First, it emphasizes the need for robust, multilevel measurement frameworks capable of capturing adaptive competence not as a set of discrete skills, but as a higher-order construct emerging from interconnected domains and contextual interactions. Second, it advocates for the integration of computational approaches, including agent-based modeling and system dynamics, to simulate healthcare processes and explore emergent system behaviors. Third, it highlights the importance of developing longitudinal and intervention-based research designs to evaluate the impact of complexity-informed strategies on outcomes such as workforce resilience, patient safety, and system performance. The Nursing Theory of CAS (NTCAS) is presented as a conceptual scaffold supporting this transition from theoretical recognition to empirical execution. The paper concludes that advancing methodological alignment with system complexity is essential for transforming complexity science in nursing from a descriptive paradigm into a predictive and actionable framework.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
