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This article presents methods for managing data-sharing processes to make it easier to produce synthesis-ready research. The authors advocate for changes in research practices that focus on synthesizability to reduce delays in implementing effective primary-study approaches. They argue that adopting such methods would have a positive impact on the translation of prevention science into policies and practices.
It is important to be able to robustly synthesize all available evidence to inform policies that seek to improve the health and well-being of individuals, families and communities. However, because evidence for prevention science comes from many diverse sources, understanding what works, for whom, and in what contexts can only be achieved through a systematic and comprehensive synthesis of evidence.
Scientists seeking to undertake evidence synthesis confront many barriers that result in uncertainty about the generalizability of intervention effectiveness. Problems include inaccurate titles/abstracts/keywords terminology (hampering literature search efforts), ambiguous reporting of study methods (resulting in inaccurate assessments of study rigor) and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). Prevention scientists also confront many other perceived and actual challenges, such as the costs of data sharing, lack of incentives, lack of awareness or training, not wanting to “give away” data or get “scooped” on findings and the necessity of ensuring participant confidentiality
To address these issues and to improve the reach of primary studies through their inclusion in evidence syntheses, the authors of this article provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. The authors use an empirical example to ground the discussion and to demonstrate ways to ensure that (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable.
The authors highlight several tools and practices that can aid authors in these efforts. Among these are using a data-driven approach for crafting titles, abstracts, and keywords, or creating a repository for each project to host all study-related data files. The article provides step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.
The authors argue that if prevention scientists share research in synthesis-ready forms, the field will likely see improvements, especially in terms of impacts on policy and practice.
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