scholarly journals Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact

2020 ◽  
Author(s):  
Emily Alden Hennessy ◽  
Rebecca Acabchuk ◽  
Pieter Andrew Arnold ◽  
Adam G. Dunn ◽  
Yong Zhi Foo ◽  
...  
Keyword(s):  
Evidence Synthesis ◽  
Science Research ◽  
Relevant Research ◽  
Scientific Impact ◽  
Related Data ◽  
What Works ◽  
Practical Guidelines ◽  
Data Files ◽  
Key Variables ◽  
Data Design

Synthesis of evidence from the totality of relevant research is essential to inform and improve prevention efforts and policy. Given the large and usually heterogeneous evidence available,reaching a thorough understanding of what works, for whom, and in what contexts, can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including: inaccurate terminology titles/abstracts/keywords (hampering literature search efforts); ambiguous reporting of study methods (resulting ininaccurate 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). To address these issues and improve the reach of primary studies through theirinclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.

scholarly journals Clustering in Wineinformatics with Attribute Selection to Increase Uniqueness of Clusters

Fermentation ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 27
Author(s):  
Jared McCune ◽  
Alex Riley ◽  
Bernard Chen
Keyword(s):  
Unsupervised Learning ◽  
Data Science ◽  
Science Research ◽  
Research Area ◽  
Attribute Selection ◽  
Short List ◽  
Wine Quality ◽  
Filtration Process ◽  
Target Region ◽  
Related Data

Wineinformatics is a new data science research area that focuses on large amounts of wine-related data. Most of the current Wineinformatics researches are focused on supervised learning to predict the wine quality, price, region and weather. In this research, unsupervised learning using K-means clustering with optimal K search and filtration process is studied on a Bordeaux-region specific dataset to form clusters and find representative wines in each cluster. 14,349 wines representing the 21st century Bordeaux dataset are clustered into 43 and 13 clusters with detailed analysis on the number of wines, dominant wine characteristics, average wine grades, and representative wines in each cluster. Similar research results are also generated and presented on 435 elite wines (wines that scored 95 points and above on a 100 points scale). The information generated from this research can be beneficial to wine vendors to make a selection given the limited number of wines they can realistically offer, to connoisseurs to study wines in a target region/vintage/price with a representative short list, and to wine consumers to get recommendations. Many possible researches can adopt the same process to analyze and find representative wines in different wine making regions/countries, vintages, or pivot points. This paper opens up a new door for Wineinformatics in unsupervised learning researches.

Making Nano Matter

2012 ◽  
Vol 37 (6) ◽  
pp. 604-626 ◽  
Author(s):  
Elena Simakova
Keyword(s):  
Social Science ◽  
Scientific Practice ◽  
Social Science Research ◽  
Science Research ◽  
The United States ◽  
University Campus ◽  
Relevant Research ◽  
Societal Relevance ◽  
Set Up ◽  
Interpretive Flexibility

The article examines science-policy conversations mediated by social science in attempts to govern, or set up terms for, scientific research. The production of social science research accounts about science faces challenges in the domains of emerging technosciences, such as nano. Constructing notions of success and failure, participants in science actively engage in the interpretation of policy notions, such as the societal relevance of their research. Industrial engagement is one of the prominent themes both in policy renditions of governable science, and in the participants’ attempts to achieve societally relevant research, often oriented into the future. How do we, as researchers, go about collecting, recording, and analyzing such future stories? I examine a series of recent interviews conducted in a number of US universities, and in particular at a university campus on the West Coast of the United States. The research engages participants through interviews, which can be understood as occasions for testing the interpretive flexibility of nano as “good” scientific practice and of what counts as societal relevance, under what circumstances and in view of what kind of audiences.

scholarly journals PRISMA2020: an R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis

2021 ◽  
Author(s):  
Neal R Haddaway ◽  
Matthew J Page ◽  
Christopher C Pritchard ◽  
Luke A McGuinness
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Science Communication ◽  
Review Process ◽  
Evidence Synthesis ◽  
R Package ◽  
Web Based ◽  
Shiny App ◽  
Data Files ◽  
Flow Diagrams

Background Reporting standards, such as PRISMA aim to ensure that the methods and results of systematic reviews are described in sufficient detail to allow full transparency. Flow diagrams in evidence syntheses allow the reader to rapidly understand the core procedures used in a review and examine the attrition of irrelevant records throughout the review process. Recent research suggests that use of flow diagrams in systematic reviews is poor and of low quality and called for standardised templates to facilitate better reporting in flow diagrams. The increasing options for interactivity provided by the Internet gives us an opportunity to support easy-to-use evidence synthesis tools, and here we report on the development of tools for the production of PRISMA 2020-compliant systematic review flow diagrams. Methods and Findings We developed a free-to-use, Open Source R package and web-based Shiny app to allow users to design PRISMA flow diagrams for their own systematic reviews. Our tools allow users to produce standardised visualisations that transparently document the methods and results of a systematic review process in a variety of formats. In addition, we provide the opportunity to produce interactive, web-based flow diagrams (exported as HTML files), that allow readers to click on boxes of the diagram and navigate to further details on methods, results or data files. We provide an interactive example here; https://driscoll.ntu.ac.uk/prisma/. Conclusions We have developed a user-friendly suite of tools for producing PRISMA 2020-compliant flow diagrams for users with coding experience and, importantly, for users without prior experience in coding by making use of Shiny. These free-to-use tools will make it easier to produce clear and PRISMA 2020-compliant systematic review flow diagrams. Significantly, users can also produce interactive flow diagrams for the first time, allowing readers of their reviews to smoothly and swiftly explore and navigate to further details of the methods and results of a review. We believe these tools will increase use of PRISMA flow diagrams, improve the compliance and quality of flow diagrams, and facilitate strong science communication of the methods and results of systematic reviews by making use of interactivity. We encourage the systematic review community to make use of these tools, and provide feedback to streamline and improve their usability and efficiency.

Measuring the Unmeasurable? Taking Stock of QoG Measures

2021 ◽  
pp. 92-114
Author(s):  
Nicholas Charron
Keyword(s):  
Social Science ◽  
Quality Indicator ◽  
Social Science Research ◽  
Science Research ◽  
Wide Scope ◽  
Scientific Impact ◽  
History Of ◽  
Cross Country ◽  
Future Work

This chapter discusses a wide scope of the available indicators of quality of government. It begins with a brief history of the development of the indicators and their scientific impact on social science research. The chapter posits a typology of the various ways in which indicators of governance can differ and implications of such differences. The chapter then reveals the degree to which contemporary cross-country indicators of corruption in particular correlate. Next, several well-established critiques of contemporary data are presented. The chapter concludes with several comments on what makes a good quality indicator and puts for several suggestions for future work in this ever-growing field.

Policy analysis by academics

2017 ◽  
Author(s):  
Marleen Brans ◽  
David Aubin ◽  
Valérie Smet
Keyword(s):  
Policy Making ◽  
Social Science Research ◽  
Science Research ◽  
Relevant Research ◽  
Education Policies ◽  
Policy Makers ◽  
Research Programmes ◽  
Structural Interfaces ◽  
Policy Problems ◽  
The Impact

Through their policy relevant research outputs and integration in policy networks, Belgian academics ‘speak truth to power’ (Wildavsky 1979) or ‘make sense together’ (Hoppe 1999) in political and public debates about policy problems and options. At the turn of the millennium, the federal and regional governments have moved to institutionalizing policy relevant research in what are called interuniversity research pillars, and middle to long term research programmes, thematically organised along the priorities decided by the respective governments. Next to these structural interfaces, there are other access points for academics to bring their expertise to policy-making. Sectoral academic experts maintain multiple relationships with knowledge brokers. They are welcome guests in opinion sections of the written and spoken media and hold positions in the strategic advisory bodies of different governments. Several of them are also active in think tanks, or act themselves as consultants in commercial university spin-offs. This chapter analyses the structural and individual access of academics to policy-making in Belgium. The empirical material is based upon documents analysis and budget information, on a study of knowledge utilisation in labour market and education policies in Belgium, and on a recent survey on the impact of social science research on Flemish policy-makers.

Integrative Psychiatry and Brain Health

2018 ◽  
Author(s):  
Andrew Weil
Keyword(s):  
Integrative Medicine ◽  
Complementary Therapies ◽  
Future Research ◽  
Evidence Based ◽  
Brain Health ◽  
Integrative Therapy ◽  
What Works ◽  
Key Points ◽  
Practical Guidelines ◽  
Integrative Psychiatry

Part of the Weil Integrative Medicine Library, this volume provides a rational and evidence-based approach to the integrative therapy of mental disorders, integrating the principles of alternative and complementary therapies into the principles and practice of conventional psychiatry and psychology. Integrative Psychiatry and Brain Health examines what works and what doesn’t and offers practical guidelines for physicians to incorporate integrative medicine into their practice and to advise patients on reasonable and effective therapies. The text discusses areas of controversy and identifies areas of uncertainty where future research is needed. Chapters also cite the best available evidence for both the safety and the efficacy of all therapies discussed. The information is presented in accessible and easy-to-read formats, including clinical pearls and key points.

scholarly journals Conclusion: Special section on modelling key variables in social science research

2016 ◽  
Vol 9 ◽  
pp. 205979911663798
Author(s):  
Roxanne Connelly ◽  
Vernon Gayle ◽  
Paul S. Lambert
Keyword(s):  
Social Science ◽  
Special Section ◽  
Social Science Research ◽  
Science Research ◽  
Key Variables

Finding What Works

2015 ◽  
Author(s):  
Thomas L. Sexton ◽  
Julie R. LaFollette
Keyword(s):  
Treatment Guidelines ◽  
Clinical Intervention ◽  
Science Research ◽  
Simple Task ◽  
Couple And Family Therapy ◽  
What Works ◽  
Literature Reviews ◽  
Client Factors ◽  
Change Mechanisms ◽  
Meta Analyses

One of the critical challenges in relationship science is translating the “science” of relationship research into the “practice” of clinical intervention. One of the major issues in this challenge is determining when something “works” or, more specifically, identifying the central criteria from which to evaluate the findings of research and determine that an intervention is ready for clinical use. This seemingly simple task is complex given that relationship science research is based on the interaction among client factors, therapeutic influence, and specific change mechanisms that lead to measurable outcomes in couple and family therapy (CFT). As a result of the complexity, determining what works can no longer be accomplished by literature reviews or meta-analyses alone. Determining what works in a clinically useful way is an important task because if clinicians are to use research it must be evaluated on components that are both methodologically sound and clinically useful. We suggest that treatment guidelines have the potential to reliably distinguish varying levels of evidence and effectively disseminate this information to practitioners, serving to close the gap between practice and research in relationship science. Thus, treatment guidelines offer a “vehicle” to move research into practice.

Design science research with focus groups – a pragmatic meta-model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Telmo Antonio Henriques ◽  
Henrique O’Neill
Keyword(s):  
Focus Groups ◽  
Design Research ◽  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Success Factor ◽  
Content Type ◽  
Meta Model ◽  
Is Implementation ◽  
Practical Guidelines

PurposeThe purpose of this research paper is to present a pragmatic and systematic approach to conduct and document Design Science Research (DSR) activities with Focus Groups (FGs), exploring its continuous usage and providing traceability between problem, requirements, solutions and artefacts.Design/methodology/approachThe approach is to conduct the research and produce the meta-model for DSR with FG, a DSR approach was adopted using a conceptual model for Action Design Research already available. The artefact is the result from a specific literature review to define requirements, a careful design and a refinement stage where it was widely used and tested in real IS implementation projects.FindingsRigorous and committed stakeholder engagement is a critical success factor in complex projects. The main outcome of this research is a specific meta-model for DSR with FG that delivers new insights and practical guidelines for academics and professionals conducting and documenting real-world research and development initiatives deep-rooted in stakeholders' participation.Research limitations/implicationsThe meta-model has been endorsed as a practical and useful artefact by the stakeholders participating in the IS projects where it was adopted. However, to fully demonstrate its capabilities and to become more robust, the model has to be further used and tested in other application situations and environments.Originality/valueThe usage of FGs in DSR has already been proposed as an effective way, either to study artefacts, to propose improvements in its design or to acknowledge the utility of those artefacts in field use. The paper provides a sound contribution to this line of research by presenting a meta-model that integrates process and data, as well as a set of practical templates and forms that may be used by researchers and practitioners to conduct their projects.

How to Know What Works Better, When

2018 ◽  
Author(s):  
Dan Honig
Keyword(s):  
International Development ◽  
Qualitative Data ◽  
Environmental Predictability ◽  
Development Organizations ◽  
What Works ◽  
Navigation Strategy ◽  
Key Variables ◽  
Environmental Unpredictability ◽  
The Relationship ◽  
Collection Methods

This chapter develops testable hypotheses of when Navigation by Judgment will be more or less successful. It develops theory as to why environmental predictability and project external verifiability play important mediating roles in the relationship between navigation strategy and success. The chapter argues that returns to Navigation by Judgment will rise as environmental unpredictability rises and as task verifiability falls. The chapter also introduces the quantitative and qualitative data that will be used in chapters 6 and 7 and discusses quantitative and qualitative data-collection methods at some length. The chapter also operationalizes for quantitative analysis the key variables, including Project Success, the propensity of international development organizations to Navigate by Judgment, and environmental unpredictability.

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