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| PRISMA 준수 우산 검토× | 체계적 문헌 고찰× | |
|---|---|---|
| 분야 | 과학계량학 | 과학계량학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2015 (umbrella review methodology); 2022 (PRIOR reporting extension) | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| 창시자≠ | Aromataris et al. (JBI); PRIOR reporting extension by Fusar-Poli et al. | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| 유형≠ | Evidence synthesis / review of systematic reviews | Evidence synthesis methodology |
| 원전≠ | Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: Methodological development, conduct and reporting of an umbrella review approach. International Journal of Evidence-Based Healthcare, 13(3), 132–140. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| 별칭 | umbrella review with PRISMA, PRIOR-guided umbrella review, overview of reviews, PRISMA umbrella overview | SLR, systematic review, evidence synthesis review, structured literature review |
| 관련 | 5 | 5 |
| 요약≠ | A PRISMA-compliant umbrella review is a structured synthesis of existing systematic reviews and meta-analyses on a topic, conducted and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines — specifically the PRIOR extension developed for umbrella reviews. By operating one level above primary research, it maps the totality of evidence, identifies convergent or contradictory findings across reviews, and quantifies evidence strength at the highest tier of the evidence hierarchy. | 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 ↗ |
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