18 research outputs found

    Transparent, Open, and Reproducible Prevention Science

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    The field of prevention science aims to understand societal problems, identify effective interventions, and translate scientific evidence into policy and practice. There is growing interest among prevention scientists in the potential for transparency, openness, and reproducibility to facilitate this mission by providing opportunities to align scientific practice with scientific ideals, accelerate scientific discovery, and broaden access to scientific knowledge. The overarching goal of this manuscript is to serve as a primer introducing and providing an overview of open science for prevention researchers. In this paper, we discuss factors motivating interest in transparency and reproducibility, research practices associated with open science, and stakeholders engaged in and impacted by open science reform efforts. In addition, we discuss how and why different types of prevention research could incorporate open science practices, as well as ways that prevention science tools and methods could be leveraged to advance the wider open science movement. To promote further discussion, we conclude with potential reservations and challenges for the field of prevention science to address as it transitions to greater transparency, openness, and reproducibility. Throughout, we identify activities that aim to strengthen the reliability and efficiency of prevention science, facilitate access to its products and outputs, and promote collaborative and inclusive participation in research activities. By embracing principles of transparency, openness, and reproducibility, prevention science can better achieve its mission to advance evidence-based solutions to promote individual and collective well-being

    Lawmakers\u27 Use of Scientific Evidence Can Be Improved

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    Core to the goal of scientific exploration is the opportunity to guide future decision-making. Yet, elected officials often miss opportunities to use science in their policymaking. This work reports on an experiment with the US Congress-evaluating the effects of a randomized, dual-population (i.e., researchers and congressional offices) outreach model for supporting legislative use of research evidence regarding child and family policy issues. In this experiment, we found that congressional offices randomized to the intervention reported greater value of research for understanding issues than the control group following implementation. More research use was also observed in legislation introduced by the intervention group. Further, we found that researchers randomized to the intervention advanced their own policy knowledge and engagement as well as reported benefits for their research following implementation

    Lawmakers' use of scientific evidence can be improved.

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    Core to the goal of scientific exploration is the opportunity to guide future decision-making. Yet, elected officials often miss opportunities to use science in their policymaking. This work reports on an experiment with the US Congress-evaluating the effects of a randomized, dual-population (i.e., researchers and congressional offices) outreach model for supporting legislative use of research evidence regarding child and family policy issues. In this experiment, we found that congressional offices randomized to the intervention reported greater value of research for understanding issues than the control group following implementation. More research use was also observed in legislation introduced by the intervention group. Further, we found that researchers randomized to the intervention advanced their own policy knowledge and engagement as well as reported benefits for their research following implementation

    Manuscripts

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    Clearinghouse Outreach

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    Outreach to evidence clearinghouses about the TRUST Initiativ

    Journal Editor Survey

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    The journal editor survey measured editors' support for the TOP Guidelines

    Expert-generated standard practice elements for evidence-based home visiting programs using a Delphi process

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    Background: States, territories, non-profits, and tribes are eligible to obtain federal funding to implement federally endorsed evidence-based home visiting programs. This represents a massive success in translational science, with $400 million a year allocated to these implementation efforts. This legislation also requires that 3% of this annual funding be allocated to tribal entities implementing home visiting in their communities. However, implementing stakeholders face challenges with selecting which program is best for their desired outcomes and context. Moreover, recent reviews have indicated that when implemented in practice and delivered at scale, many evidence-based home visiting programs fail to replicate the retention rates and effects achieved during clinical trials. To inform program implementers and better identify the active ingredients in home visiting programs that drive significant impacts, we aimed to develop an expert derived consensus taxonomy on the elements used in home visiting practice that are essential to priority outcome domains. Methods: We convened a panel of 16 experts representing researchers, model representatives, and program implementers using a Delphi approach. We first elicited standard practice elements (SPEs) using open-ended inquiry, then compared these elements to behavior change techniques (BCTs) given their general importance in the field of home visiting; and finally rated their importance to 10 outcome domains. Results: Our process identified 48 SPEs derived from the panel, with 83 additional BCTs added based on the literature. Six SPEs, mostly related to home visitor characteristics and skills, were rated essential across all outcome domains. Fifty-three of the 83 BCTs were rated unnecessary across all outcome domains. Conclusions: This work represents the first step in a consensus-grounded taxonomy of techniques and strategies necessary for home visiting programs and provides a framework for future hypothesis testing and replication studies.</p
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