Automated tools for systematic review screening methods: an application of machine learning for sexual orientation and gender identity measurement in health research.

Journal: Journal of the Medical Library Association : JMLA
PMID: 39975502

Abstract

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.

Authors

  • Ashleigh J Rich
    Ashleigh.rich@duke.edu, School of Nursing, Duke University, Durham, NC.
  • Emma L McGorray
    EmmaMcgorray2023@u.northwestern.edu, Department of Psychology, Northwestern University, Evanston, IL.
  • Carrie Baldwin-SoRelle
    chbs@email.unc.edu, Health Sciences Library, University of North Carolina Chapel Hill, Chapel Hill, NC.
  • Michelle Cawley
    mcawley@email.unc.edu, Health Sciences Library, University of North Carolina Chapel Hill, Chapel Hill, NC.
  • Karen Grigg
    kgrigg@email.unc.edu, Health Sciences Library, University of North Carolina Chapel Hill, Chapel Hill, NC.
  • Lauren B Beach
    lauren.beach@northwestern.edu, Department of Medical Social Sciences, Department of Preventative Medicine, Northwestern University, Evanston, IL.
  • Gregory Phillips
    Glp2@northwestern.edu, Department of Medical Social Sciences, Department of Preventative Medicine, Northwestern University, Evanston, IL.
  • Tonia Poteat
    tonia.poteat@duke.edu, School of Nursing, Duke University, Durham, NC.

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