Locating sex- and gender-specific data in health promotion research: evaluating the sensitivity and precision of published filters

Diane L. Lorenzetti, Yongtao Lin


Objective: This study explored the effectiveness of search filters in identifying sex- and gender-specific data in health promotion studies that are indexed in MEDLINE.

Methods: Literature searches were conducted to identify studies on patient or consumer attitudes and behaviors toward colorectal cancer screening, nutritional labeling, and influenza vaccination. Publications reporting sex- or gender-specific outcome data constituted the gold standards for this study. The sensitivity and precision of previously published gender-specific filters, as well as individual filter component terms, were calculated and compared with values identified in prior studies.

Results: The sensitivity and precision of published sex or gender filters varied across topics. Sensitivity values ranged from 14.3% to 92.5%, while precision varied from 17.9% to 51.4%. These filters were less sensitive and less precise in their identification of relevant studies than has been reported in previous studies. Further, while the MEDLINE Medical Subject Headings (MeSH) term “Sex Factors” achieved the greatest average precision (59.3%) of any individual filter term, the MEDLINE check tag “Female” returned the highest average sensitivity (90.1%), with an average precision of 25.0% across topics.

Conclusions: Although search filters can facilitate the identification of research evidence to enable decision making, variability in study abstracting and indexing can limit the generalizability and usability of these filters. This potential for variability should be considered when deciding to incorporate a search filter into any literature search. This research highlights the importance of this awareness when developing strategies for searching the published literature and the potential value of supplementing database searching with other methods of study identification.


Health Promotion; Information Retrieval; Methodological Filters; Sex Factors

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DOI: https://doi.org/10.5195/jmla.2017.236


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Copyright (c) 2017 Diane L. Lorenzetti, PhD, MLS, Yongtao Lin

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