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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Протоколно-базирана метаетнография× | Систематичен литературен обзор× | |
|---|---|---|
| Област | Наукометрия | Наукометрия |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1988 (meta-ethnography); protocol-based practice formalised 2010s | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| Създател≠ | Noblit & Hare (meta-ethnography); protocol registration formalised through PROSPERO and eMERGe guidance | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| Тип≠ | Qualitative evidence synthesis with pre-registered protocol | Evidence synthesis methodology |
| Основополагащ източник≠ | Noblit, G. W., & Hare, R. D. (1988). Meta-Ethnography: Synthesizing Qualitative Studies. Sage. ISBN: 978-0803930742 | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| Други названия | pre-registered meta-ethnography, prospero meta-ethnography, registered qualitative synthesis, protocol-driven meta-ethnography | SLR, systematic review, evidence synthesis review, structured literature review |
| Свързани | 5 | 5 |
| Резюме≠ | Protocol-based meta-ethnography is a structured qualitative evidence synthesis that follows Noblit and Hare's meta-ethnography method while requiring a pre-registered, publicly available protocol — typically on PROSPERO — before the review is conducted. Pre-registration constrains post-hoc decision-making, enhances methodological transparency, and aligns qualitative synthesis with the rigour standards now expected by leading journals and funders. | 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. |
| ScholarGateНабор от данни ↗ |
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