Diversified innovations in the health sciences: Proposal for a Diversity Minimal Item Set (DiMIS)

Science strives to provide high-quality evidence for all members of society, but there continues to be a considerable gender and diversity data gap, i.e., a systematic lack of data for traditionally underrepresented groups. Gender and other diversity domains are related to morbidity, mortality, and social and economic participation, yet measures as well as evidence regarding how these domains intersect are missing. We propose a brief, efficient Diversity Minimal Item Set (DiMIS) for routine data collection in empirical studies to contribute to closing the diversity and gender data gap. We focus on the example of health but consider the DiMIS applicable across scientific disciplines. To identify items for the DiMIS across diversity domains, we performed an extensive literature search and conducted semi-structured interviews with scientific experts and community stakeholders in nine diversity domains. Using this information, we created a minimal item set of self-report survey items for each domain. Items covering nine diversity domains as well as discrimination experiences were compiled from a variety of sources and modified as recommended by experts. The DiMIS focuses on an intersectional approach, i.e., studying gender, age, socioeconomic status, care responsibilities, sexual orientation, ethnicity, religion, disability, mental and physical health, and their intersections. It allows for data sets with comparable assessments of gender and diversity across multiple projects to be combined, creating samples large enough for meaningful analyses. In proposing the DiMIS, we hope to advance the conversation about closing the gender and diversity data gap in science. [Display omitted] •The sciences still show data gaps regarding gender and diversity.•We propose a widely applicable Diversity Minimal Item Set (DiMIS).•The DiMIS is brief, easy to use, and captures 9 diversity domains.•Comparable diversity assessments allow meaningful data combination.•Combining data allows intersectional analyses promising more diversified innovations.