Automated tools for systematic review screening methods: an application of machine learning for sexual orientation and gender identity measurement in health research.
Objective: Sexual and gender minority (SGM) populations experience health disparities compared to heterosexual and cisgender populations. The development of accurate, comprehensive sexual orientation and gender identity (SOGI) measures is fundamental to quantify and address SGM disparities, which fi...
| Publicado en: | Journal of the Medical Library Association Vol. 113; no. 1; pp. 31 - 39 |
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| Autores principales: | , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
University of Pittsburgh, University Library System
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182552267&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182552267 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15365050 PI8 jtl: Journal of the Medical Library Association issn: 15365050 maglogo: N pubinfo: dt: Jan2025 vid: 113 iid: 1 pid: 60406 pub: University of Pittsburgh, University Library System place: Pittsburgh, Pennsylvania artinfo: ui: 182552267 182552267 182552267 10.5195/jmla.2025.1860 182552267 ppf: 31 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Automated tools for systematic review screening methods: an application of machine learning for sexual orientation and gender identity measurement in health research. aug: au: Rich, Ashleigh J. McGorray, Emma L. Baldwin-SoRelle, Carrie Cawley, Michelle Grigg, Karen Beach, Lauren B. Phillips II, Gregory Poteat, Tonia affil: School of Nursing, Duke University, Durham, NC sug: subj: Automation Methods Machine Learning Methods Sexual Orientation Evaluation Gender Identity Evaluation Health Services Research Human Funding Source Comparative Studies Descriptive Statistics Document Analysis Systematic Review Health Literacy Collaboration Librarians Sexual and Gender Minorities Health Status Healthcare Disparities Database Quality Database Management Software Clustering Algorithms ab: Objective: Sexual and gender minority (SGM) populations experience health disparities compared to heterosexual and cisgender populations. The development of accurate, comprehensive sexual orientation and gender identity (SOGI) measures is fundamental to quantify and address SGM disparities, which first requires identifying SOGI-related research. As part of a larger project reviewing and synthesizing how SOGI has been assessed within the health literature, we provide an example of the application of automated tools for systematic reviews to the area of SOGI measurement. Methods: In collaboration with research librarians, a three-phase approach was used to prioritize screening for a set of 11,441 SOGI measurement studies published since 2012. In Phase 1, search results were stratified into two groups (title with vs. without measurement-related terms); titles with measurement-related terms were manually screened. In Phase 2, supervised clustering using DoCTER software was used to sort the remaining studies based on relevance. In Phase 3, supervised machine learning using DoCTER was used to further identify which studies deemed low relevance in Phase 2 should be prioritized for manual screening. Results: 1,607 studies were identified in Phase 1. Across Phases 2 and 3, the research team excluded 5,056 of the remaining 9,834 studies using DoCTER. In manual review, the percentage of relevant studies in results screened manually was low, ranging from 0.1 to 7.8 percent. Conclusions: Automated tools used in collaboration with research librarians have the potential to save hundreds of hours of human labor in large-scale systematic reviews of SGM health research. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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