Database selection and data gathering methods in systematic reviews of qualitative research regarding diabetes mellitus - an explorative study

Abstract

Background Systematic reviews (SRs) are considered one of the most reliable types of studies in evidence-based medicine. SRs rely on a comprehensive and systematic data gathering, including the search of academic literature databases. This study aimed to investigate which combination of databases would result in the highest overall recall rate of references when conducting SRs of qualitative research regarding diabetes mellitus. Furthermore, we aimed to investigate the current use of databases and other sources for data collection. Methods Twenty-six SRs (published between 2010 and 2020) of qualitative research regarding diabetes mellitus, located through PubMed, met the inclusion criteria. References of the SRs were systematically hand searched in the six academic literature databases CINAHL, MEDLINE/PubMed, PsycINFO, Embase, Web of Science, and Scopus and the academic search engine Google Scholar. Recall rates were calculated using the total number of included references retrieved by the database or database combination divided by the total number of included references, given in percentage. Results The SRs searched five databases on average (range two to nine). MEDLINE/PubMed was the most commonly searched database (100% of SRs). In addition to academic databases, 18 of the 26 (69%) SRs hand searched the reference lists of included articles. This technique resulted in a median (IQR) of 2.5 (one to six) more references being included per SR than by database searches alone. 27 (5.4%) references were found only in one of six databases (when Google Scholar was excluded), with CINAHL retrieving the highest number of unique references (n = 15). The combinations of MEDLINE/PubMed and CINAHL (96.4%) and MEDLINE/PubMed, CINAHL, and Embase (98.8%) yielded the highest overall recall rates, with Google Scholar excluded. Conclusions We found that the combinations of MEDLINE/PubMed and CINAHL and MEDLINE/PubMed, CINAHL, and Embase yielded the highest overall recall rates of references included in SRs of qualitative research regarding diabetes mellitus. However, other combinations of databases yielded corresponding recall rates and are expected to perform comparably. Google Scholar can be a useful supplement to traditional scientific databases to ensure an optimal and comprehensive retrieval of relevant references.

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