Shifting the paradigm of research-to-policy impact: Infrastructure for improving researcher engagement and collective action

Abstract

The body of scientific knowledge accumulated by the scholarly disciplines such as Developmental Psychopathology can achieve meaningful public impact if wielded and used in policy decision-making. Scientific study of how policymakers use research evidence underscores the need for researchers’ policy engagement; however, barriers in the academy create conditions in which there is a need for infrastructure that increases the feasibility of researchers’ partnership with policymakers. This need led to the development of the Research-to-Policy Collaboration model, a systematic approach for developing “boundary spanning” infrastructure, which has been experimentally tested and shown to improve policymakers’ use of research evidence and bolster researchers’ policy skills and engagement. This paper presents original research regarding the optimization of the RPC model, which sought to better serve and engage scholars across the globe. Trial findings shed light on ways to improve conditions that make good use of researchers’ time for policy engagement via a virtual platform and enhanced e-communications. Future directions, implications, and practical guidelines for how scientists can engage in the political process and improve the impact of a collective discipline are also discussed.

Integrating data science and neuroscience in developmental psychopathology: Formative examples and future directions

“…The multiple perspectives published in this special issue honoring the lifetime contributions of Dr Cicchetti beautifully illustrate ways to continue progress. This exciting future work includes investigating prenatal and intergenerational influences on development (Beeghly, 2024;Bush, 2024), advancing the frontiers of resilience research (Masten, 2024), growing interdisciplinary training (Gotlib et al, 2024), expanding participant representation and inclusivity (Tyrell et al, 2024), and informing the program and policy choices of governmental and non-governmental organizations (Scott et al, 2024). Here, we contribute our perspective that data science and machine learning techniques are valuable tools for the advancement of developmental psychopathology research.…”