Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Библиометрический нарративный обзор× | Обзор области применения (Scoping Review)× | |
|---|---|---|
| Область | Наукометрия | Наукометрия |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2017 (bibliometrix package); narrative review methodology is older | 2005 |
| Автор метода≠ | Aria & Cuccurullo (bibliometrix R package); narrative review as a traditional form predates this tool | Hilary Arksey & Lisa O'Malley |
| Тип≠ | Mixed quantitative-qualitative review methodology | Evidence synthesis review design |
| Основополагающий источник≠ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ | Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. DOI ↗ |
| Другие названия≠ | bibliometrix narrative review, R-bibliometrix narrative synthesis, quantitative-assisted narrative review | scoping study, literature scoping, evidence mapping review, rapid evidence map |
| Связанные | 6 | 6 |
| Сводка≠ | 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 scoping review is a systematic evidence-synthesis method that maps the breadth and nature of research on a topic — identifying key concepts, evidence types, and gaps — without necessarily appraising study quality or pooling effect sizes. Developed by Arksey and O'Malley (2005) and refined by Levac and colleagues (2010), it is particularly valuable for emerging or heterogeneous fields where a full systematic review would be premature or infeasible. |
| ScholarGateНабор данных ↗ |
|
|