Módszerek összehasonlítása

	Mapping Review	Szisztematikus irodalomkutatás – SLR
Tudományterület	Tudománymetria	Tudománymetria
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	Late 1990s–2000s; major methodological formalization ~2010s	1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)
Megalkotó	Buckland & Gann (1998); formalized by systematic review community (Campbell Collaboration, Collaboration for Environmental Evidence)	Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)
Típus	Systematic evidence mapping methodology	Evidence synthesis methodology
Alapmű	James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environmental Evidence, 5(1), 7.	Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401.
Alternatív nevek	evidence map, systematic map, research map, literature map	SLR, systematic review, evidence synthesis review, structured literature review
Kapcsolódó	6	5
Összefoglaló	A mapping review (also called a systematic map or evidence map) is a form of systematic review that aims to chart the extent, range, and nature of evidence on a broad topic rather than synthesize findings into a single pooled answer. It categorizes studies by key dimensions — such as intervention type, population, outcome, and study design — and presents the resulting landscape visually and tabularly so that researchers and practitioners can identify clusters of evidence, knowledge gaps, and priorities for future primary research or deeper synthesis.	A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic.