Systematic review methods
Structured approaches for answering focused questions through comprehensive searching, screening, appraisal, synthesis, and interpretation.
Rigour, clarity, transparency
Synthesis Methods
Evidence synthesis depends on formal, explicit, transparent, and reproducible methods that help bring research findings together in a trustworthy way.
Methods make evidence usable
Transparent approaches help readers understand how conclusions were reached.
Introduction
Strong methods help make synthesis reliable and useful.
Evidence synthesis is more than a summary of studies. It uses planned and documented methods to identify, select, assess, analyse, and communicate research evidence.
Formal and explicit methods help reduce bias, improve transparency, and make it easier for readers, practitioners, and decision makers to understand the strengths and limits of the evidence.
Methods focus
Approaches that support different evidence needs.
Different questions require different synthesis designs, but all benefit from transparency, careful judgement, and clear reporting.
Evidence mapping
Methods that describe where evidence exists, how it is distributed, and where important gaps may remain.
Rapid reviews
Time-sensitive synthesis approaches designed to support decisions while remaining clear about scope, shortcuts, and limitations.
Living evidence synthesis
Approaches for monitoring new research and updating syntheses when new evidence may change understanding or decisions.
Reporting standards
Guidance that helps authors describe methods and findings clearly, consistently, and transparently.
Methodological transparency
Practices that make decisions, assumptions, sources, eligibility criteria, and analytical choices visible to readers.
Quality and rigour
Quality comes from both process and judgement.
Rigorous synthesis methods help reduce avoidable bias, but quality also depends on careful judgement, relevant expertise, transparent decisions, and useful communication.
ESI supports method exchange that helps synthesis organisations improve practice while recognising that methods must be fit for purpose, proportionate to the question, and useful for the intended audience.
Cross-sector methods
Shared principles across diverse evidence communities.
Evidence synthesis methods are used across sectors, with adaptations for different questions, evidence types, and decision contexts.
Human health
Methods for interventions, services, systems, diagnostics, implementation, and public health questions.
Conservation
Approaches for synthesising ecological, environmental, and conservation management evidence.
Education
Methods for learning, teaching, equity, education policy, and programme effectiveness questions.
Veterinary medicine
Synthesis methods supporting animal health, welfare, clinical practice, and applied veterinary decisions.
Policy research
Approaches that connect research evidence with policy options, implementation contexts, and public needs.
Interdisciplinary synthesis
Methods that help evidence communities work across disciplines, evidence types, and stakeholder priorities.
FAQ
Common questions about synthesis methods.
These questions introduce key ideas behind transparent and rigorous evidence synthesis methods.
Explicit methods make it clear how studies were found, selected, assessed, and interpreted. This helps readers understand the basis of the synthesis and judge its reliability.
A systematic review usually answers a focused question. An evidence map describes the volume, distribution, and characteristics of evidence across a broader topic.
Rapid reviews are useful when decision makers need timely evidence. They should still use transparent and structured methods that match the available time and purpose.
Living evidence synthesis is an approach where evidence is monitored and updated as new relevant studies become available.
Methods collaboration
Interested in evidence synthesis methods?
Explore the foundations of evidence synthesis or contact ESI to discuss methods collaboration, training, reporting standards, and cross-sector learning.