Порівняння методів

	Польове картографування мета-етнографії	Огляд картографування
Галузь	Наукометрія	Наукометрія
Родина	Process / pipeline	Process / pipeline
Рік появи	1988 (meta-ethnography); field-mapping application 2000s–2010s	Late 1990s–2000s; major methodological formalization ~2010s
Автор методу	Noblit & Hare (meta-ethnography base); field-mapping frame developed in review methodology literature	Buckland & Gann (1998); formalized by systematic review community (Campbell Collaboration, Collaboration for Environmental Evidence)
Тип	Qualitative evidence synthesis with field-mapping scope	Systematic evidence mapping methodology
Основоположне джерело	Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing Qualitative Studies. Sage. ISBN: 978-0803930599	James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environmental Evidence, 5(1), 7.
Інші назви	field-mapping qualitative synthesis, scoping meta-ethnography, field-mapping qualitative meta-synthesis, landscape meta-ethnography	evidence map, systematic map, research map, literature map
Пов'язані	6	6
Підсумок	Field-mapping meta-ethnography combines the breadth of a field-mapping (scoping) review with the interpretive synthesis power of meta-ethnography. It first maps the full landscape of qualitative studies on a topic to understand what has been studied and how, then applies Noblit and Hare's seven-step meta-ethnographic synthesis to generate second-order and third-order constructs that represent the accumulated qualitative evidence across that field.	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.