Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science

In their recent perspective published in Patterns, Maggie Delano and Kendra Albert highlight the limitations of sex and gender data classification in health systems and show how this contributes to the marginalization of trans and non-binary individuals. They provide recommendations to improve incor...

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Detalles Bibliográficos
Autores principales: Delano, Maggie, Albert, Kendra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
People of Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403352/
https://www.ncbi.nlm.nih.gov/pubmed/36033588
http://dx.doi.org/10.1016/j.patter.2022.100573
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author Delano, Maggie
Albert, Kendra
author_facet Delano, Maggie
Albert, Kendra
author_sort Delano, Maggie
collection PubMed
description In their recent perspective published in Patterns, Maggie Delano and Kendra Albert highlight the limitations of sex and gender data classification in health systems and show how this contributes to the marginalization of trans and non-binary individuals. They provide recommendations to improve incorporating gender data into healthcare algorithms. Here they discuss their collaboration and how it enabled this cross-disciplinary research.
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spelling pubmed-94033522022-08-26 Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science Delano, Maggie Albert, Kendra Patterns (N Y) People of Data In their recent perspective published in Patterns, Maggie Delano and Kendra Albert highlight the limitations of sex and gender data classification in health systems and show how this contributes to the marginalization of trans and non-binary individuals. They provide recommendations to improve incorporating gender data into healthcare algorithms. Here they discuss their collaboration and how it enabled this cross-disciplinary research. Elsevier 2022-08-12 /pmc/articles/PMC9403352/ /pubmed/36033588 http://dx.doi.org/10.1016/j.patter.2022.100573 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle People of Data
Delano, Maggie
Albert, Kendra
Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science
title Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science
title_full Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science
title_fullStr Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science
title_full_unstemmed Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science
title_short Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science
title_sort two researchers share how their cross disciplinary collaboration enables work to guide the future of data science
topic People of Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403352/
https://www.ncbi.nlm.nih.gov/pubmed/36033588
http://dx.doi.org/10.1016/j.patter.2022.100573
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