Порівняння методів

	Rapid Evidence Assessment	Реалістичний синтез
Галузь	Public Policy	Синтез доказів
Родина	Process / pipeline	Process / pipeline
Рік появи	2013	2005
Автор методу	UK Government Social Research Service with the EPPI-Centre	Ray Pawson (2005)
Тип	Time-constrained systematic evidence synthesis	Framework
Основоположне джерело	Government Social Research Service (2013). Rapid Evidence Assessment Toolkit. London: Civil Service / GSR, developed with the EPPI-Centre.	Pawson, R., Greenhalgh, T., Harvey, G., & Walshe, K. (2005). Realist review—a new method of systematic review designed for complex policy and programme evaluation. Journal of Health Services Research & Policy, 10(S1), 21–35.
Інші назви	REA, Rapid Evidence Review, Rapid Evidence Synthesis	Realist Review, CMO Configuration, Mechanism-Based Synthesis
Пов'язані	2	1
Підсумок	A rapid evidence assessment (REA) is a way of getting on top of the available research evidence on a policy question as comprehensively as possible within the constraints of a short timetable. It uses the systematic and transparent methods of a full systematic review — explicit search strategies, inclusion criteria and quality appraisal — but deliberately limits the breadth of one or more stages to fit the time and resources available. Promoted in the UK by the Government Social Research Service and the EPPI-Centre, the REA was designed to give policymakers rigorous, accountable evidence syntheses on timescales that conventional systematic reviews cannot meet.	Realist synthesis is a theory-driven, interpretive method for evidence synthesis developed by Ray Pawson (2005) that focuses on understanding HOW and WHY interventions work, rather than WHETHER they work. Grounded in realist philosophy, realist synthesis examines Context-Mechanism-Outcome (CMO) configurations: how specific contextual conditions activate mechanisms that produce outcomes. Unlike traditional systematic reviews, which typically answer 'Does intervention X reduce outcome Y?', realist synthesis asks 'Under what conditions, through what mechanisms, for which populations does X work?' This approach is particularly valuable for complex interventions (policies, programs, multi-component treatments) where effectiveness varies dramatically across contexts, and for understanding why interventions succeed in some settings but fail in others.