Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Meta-etnografia ya ramani ya shamba× | Mapitio ya Kitaalam ya Vitabu× | |
|---|---|---|
| Nyanja | Saintometriki | Saintometriki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1988 (meta-ethnography); field-mapping application 2000s–2010s | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| Mwanzilishi≠ | Noblit & Hare (meta-ethnography base); field-mapping frame developed in review methodology literature | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| Aina≠ | Qualitative evidence synthesis with field-mapping scope | Evidence synthesis methodology |
| Chanzo asilia≠ | Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing Qualitative Studies. Sage. ISBN: 978-0803930599 | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| Majina mbadala | field-mapping qualitative synthesis, scoping meta-ethnography, field-mapping qualitative meta-synthesis, landscape meta-ethnography | SLR, systematic review, evidence synthesis review, structured literature review |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Field-mapping meta-ethnography combines the breadth of a field-mapping (scoping) review with the interpretive synthesis power of meta-ethnography. It first maps the full landscape of qualitative studies on a topic to understand what has been studied and how, then applies Noblit and Hare's seven-step meta-ethnographic synthesis to generate second-order and third-order constructs that represent the accumulated qualitative evidence across that field. | 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. |
| ScholarGateSeti ya data ↗ |
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