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| Bibliometrix-Assisted Narrative Review× | 체계적 문헌 고찰× | |
|---|---|---|
| 분야 | 과학계량학 | 과학계량학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2017 (bibliometrix package); narrative review methodology is older | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| 창시자≠ | Aria & Cuccurullo (bibliometrix R package); narrative review as a traditional form predates this tool | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| 유형≠ | Mixed quantitative-qualitative review methodology | Evidence synthesis methodology |
| 원전≠ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| 별칭≠ | bibliometrix narrative review, R-bibliometrix narrative synthesis, quantitative-assisted narrative review | SLR, systematic review, evidence synthesis review, structured literature review |
| 관련≠ | 6 | 5 |
| 요약≠ | A bibliometrix-assisted narrative review combines the quantitative field-mapping capabilities of the bibliometrix R package with the interpretive flexibility of a traditional narrative review. Bibliometric indicators — publication trends, author and country productivity, co-citation networks, keyword co-occurrence — are computed and visualised first to orient the reviewer, then a discursive, thematic narrative synthesises the intellectual content of key sources. The result is a structured yet flexible overview of a field that is more transparent and reproducible than a purely informal narrative. | 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. |
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