Σύγκριση μεθόδων

	Επισκόπηση πεδίου χαρτογράφησης	Συστηματική Βιβλιογραφική Ανασκόπηση
Πεδίο	Επιστημομετρία	Επιστημομετρία
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2005 (foundational framework); field-mapping purpose formalised c. 2015–2018	1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)
Δημιουργός	Arksey & O'Malley (scoping review framework); field-mapping purpose formalised by Munn et al. and Peters et al.	Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)
Τύπος	Evidence synthesis — systematic review variant	Evidence synthesis methodology
Θεμελιώδης πηγή	Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic review and scoping review approach. BMC Medical Research Methodology, 18, 143.	Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401.
Εναλλακτικές ονομασίες	field-mapping scoping study, evidence-mapping scoping review, field map review, scoping review for field mapping	SLR, systematic review, evidence synthesis review, structured literature review
Συναφείς	6	5
Σύνοψη	A field-mapping scoping review is a purposive variant of the scoping review in which the overarching goal is to chart the conceptual and empirical landscape of a research field — identifying what has been studied, by whom, using which methods, and where knowledge gaps remain. It follows the Arksey and O'Malley scoping framework but is explicitly oriented toward producing a structured map of a field rather than answering a focused clinical or policy question.	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.