Four principles to make evidence synthesis more useful for policy

Donnelly, Christl A.; Boyd, Ian L.; Campbell, Philip; Craig, Claire; Vallance, Patrick; Walport, Mark; Whitty, Christopher J. M.; Woods, E; Wormald, Chris

doi:10.1038/d41586-018-05414-4

Cited by 108 publications

(97 citation statements)

References 2 publications

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“…This is supported by the call from nine influentially placed coauthors, including former and current chief scientific advisers to the British government, for information syntheses that are inclusive, rigorous, transparent, and accessible for policy makers,19 a call we consider long overdue 20…”

Section: Where Are We Now?mentioning

confidence: 99%

Research waste is still a scandal—an essay by Paul Glasziou and Iain Chalmers

Glasziou

Chalmers²

2018

BMJ

190

178

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Section: Where Are We Now?mentioning

confidence: 99%

Research waste is still a scandal—an essay by Paul Glasziou and Iain Chalmers

Glasziou

Chalmers²

2018

BMJ

190

178

View full text Add to dashboard Cite

show abstract

“…However, decision analysis would be more inclusive, rigorous, and transparent (cf. Donnelly et al, 2018), and would better fulfill the ideals of "evidence-based practice" (e.g., Pullin and Knight, 2003;Sutherland et al, 2004), if it were based on robust methods of evidence synthesis.…”

Section: Multiple-criteria Decision Analysis: What Evidence Is Needed?mentioning

confidence: 99%

Evidence Synthesis as the Basis for Decision Analysis: A Method of Selecting the Best Agricultural Practices for Multiple Ecosystem Services

Shackelford¹,

Kelsey²,

Sutherland³

et al. 2019

Front. Sustain. Food Syst.

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Agricultural management practices have impacts not only on crops and livestock, but also on soil, water, wildlife, and ecosystem services. Agricultural research provides evidence about these impacts, but it is unclear how this evidence should be used to make decisions. Two methods are widely used in decision making: evidence synthesis and decision analysis. However, a system of evidence-based decision making that integrates these two methods has not yet been established. Moreover, the standard methods of evidence synthesis have a narrow focus (e.g., the effects of one management practice), but the standard methods of decision analysis have a wide focus (e.g., the comparative effectiveness of multiple management practices). Thus, there is a mismatch between the outputs from evidence synthesis and the inputs that are needed for decision analysis. We show how evidence for a wide range of agricultural practices can be reviewed and summarized simultaneously ("subject-wide evidence synthesis"), and how this evidence can be assessed by experts and used for decision making ("multiple-criteria decision analysis"). We show how these methods could be used by Shackelford et al. Evidence Synthesis for Decision Analysis The Nature Conservancy (TNC) in California to select the best management practices for multiple ecosystem services in Mediterranean-type farmland and rangeland, based on a subject-wide evidence synthesis that was published by Conservation Evidence (www. conservationevidence.com). This method of "evidence-based decision analysis" could be used at different scales, from the local scale (farmers deciding which practices to adopt) to the national or international scale (policy makers deciding which practices to support through agricultural subsidies or other payments for ecosystem services). We discuss the strengths and weaknesses of this method, and we suggest some general principles for improving evidence synthesis as the basis for multi-criteria decision analysis.

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“…[18] Synthesis of evidence A range of types of evidence is synthesised in an accessible way using systematic, rigorous, transparent methods. [28] These syntheses include assessment of confidence in the evidence, using commonly accepted methods such as GRADE [29] and GRADE-CERQual, [22,30] and systematic identification of gaps in the evidence. Syntheses are conducted on platforms that facilitate data sharing across the evidence ecosystem, including through the use of structured terminologies and data.…”

Section: Generation Of Relevant Evidencementioning

confidence: 99%