摘要：Objective Mixed studies reviews include empirical studies with diverse designs. Given that identifying relevant studies for such reviews is time consuming, a mixed filter was developed. Methods The filter was used for six journals from three disciplines. For each journal, database records were coded “empirical” (relevant) when they mentioned a research question or objective, data collection, analysis, and results. We measured precision (proportion of retrieved documents being relevant), sensitivity (proportion of relevant documents retrieved), and specificity (proportion of nonrelevant documents not retrieved). Results Records were coded with and without the filter, and descriptive statistics were performed, suggesting the mixed filter has high sensitivity. Keywords and Medical Subject Headings (MeSH) Database, Bibliographic; Review, Systematic; Information Retrieval Systems; Empirical Research; Medical Informatics An increasing number of researchers, practitioners, and policy makers are using systematic reviews to keep their knowledge up-to-date in the current context of managing a rapidly growing number of scientific publications [ 1 ]. In a mixed studies review, a team of reviewers reviews all types of empirical research (qualitative, quantitative, or mixed methods) concurrently to develop a breadth and depth of understanding of scientific knowledge [ 2 ]. Mixed studies reviews are a type of systematic review that is becoming popular in all health disciplines because they can address complex research questions [ 3 ]. Significant methodological advancement of mixed studies reviews have been seen in the past decade with the development of numerous synthesis methods for qualitative and quantitative evidence as well as frameworks for mixing evidence [ 2 , 4 – 8 ]. Furthermore, guidance for researchers designing, conducting, and reporting systematic mixed studies reviews has been developed and is accessible in an open-access format [ 9 ]. The identification of potentially relevant studies in bibliographic databases constitutes a key stage of reviews. Because of the high number of scientific publications (e.g., estimated at more than 50 million), these bibliographic searches now yield thousands of records that require manual screening for relevance by the reviewers [ 10 , 11 ]. Thus, the identification of potentially relevant studies is time consuming and labor intensive in systematic reviews. Traditional search strategies in bibliographic databases have high specificity and sensitivity for finding randomized controlled trials but are limited for other types of research studies [ 12 , 13 ]. In addition, mixed studies reviews search strategies yield a high number of irrelevant records, such as nonempirical work (e.g., commentaries, editorials, and opinion letters). The manual screening of thousands of irrelevant records is an extremely time- and resource-consuming process [ 14 ]. However, there is no research on the best strategies for identifying qualitative, quantitative, and mixed methods studies. Such research has the potential to greatly facilitate mixed studies reviews. To our knowledge, there is no database filter to retrieve studies with diverse qualitative, quantitative, and mixed methods research designs. The authors' research objective was to develop and evaluate a database filter to retrieve studies with diverse qualitative, quantitative, and mixed methods research designs. Our research question was: What are the precision, sensitivity, and specificity of this filter?
关键词：Keywords and Medical Subject Headings (MeSH) Database; Bibliographic; Review; Systematic; Information Retrieval Systems; Empirical Research; Medical Informatics
