방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 필드 매핑 범위 검토× | 체계적 문헌 고찰× | |
|---|---|---|
| 분야 | 과학계량학 | 과학계량학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2005 (foundational framework); field-mapping purpose formalised c. 2015–2018 | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| 창시자≠ | 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) |
| 유형≠ | Evidence synthesis — systematic review variant | Evidence synthesis methodology |
| 원전≠ | 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. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| 별칭 | 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 |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|