<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="andrews-etal-2023-intersectionality">
    <titleInfo>
        <title>Intersectionality and Testimonial Injustice in Medical Records</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kenya</namePart>
        <namePart type="family">Andrews</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bhuvni</namePart>
        <namePart type="family">Shah</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lu</namePart>
        <namePart type="family">Cheng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Clinical Natural Language Processing Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tristan</namePart>
            <namePart type="family">Naumann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Asma</namePart>
            <namePart type="family">Ben Abacha</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kirk</namePart>
            <namePart type="family">Roberts</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rumshisky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Detecting testimonial injustice is an essential element of addressing inequities and promoting inclusive healthcare practices, many of which are life-critical. However, using a single demographic factor to detect testimonial injustice does not fully encompass the nuanced identities that contribute to a patient’s experience. Further, some injustices may only be evident when examining the nuances that arise through the lens of intersectionality. Ignoring such injustices can result in poor quality of care or life-endangering events. Thus, considering intersectionality could result in more accurate classifications and just decisions. To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e.g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e.g. gender and race) make a difference in uncovering testimonial injustice. From our analysis we found that with intersectionality we can better see disparities in how subgroups are treated and there are differences in how someone is treated based on the intersection of their demographic attributes. This has not been previously studied in clinical records, nor has it been proven through empirical study.</abstract>
    <identifier type="citekey">andrews-etal-2023-intersectionality</identifier>
    <identifier type="doi">10.18653/v1/2023.clinicalnlp-1.39</identifier>
    <location>
        <url>https://aclanthology.org/2023.clinicalnlp-1.39</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>358</start>
            <end>372</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Intersectionality and Testimonial Injustice in Medical Records
%A Andrews, Kenya
%A Shah, Bhuvni
%A Cheng, Lu
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F andrews-etal-2023-intersectionality
%X Detecting testimonial injustice is an essential element of addressing inequities and promoting inclusive healthcare practices, many of which are life-critical. However, using a single demographic factor to detect testimonial injustice does not fully encompass the nuanced identities that contribute to a patient’s experience. Further, some injustices may only be evident when examining the nuances that arise through the lens of intersectionality. Ignoring such injustices can result in poor quality of care or life-endangering events. Thus, considering intersectionality could result in more accurate classifications and just decisions. To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e.g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e.g. gender and race) make a difference in uncovering testimonial injustice. From our analysis we found that with intersectionality we can better see disparities in how subgroups are treated and there are differences in how someone is treated based on the intersection of their demographic attributes. This has not been previously studied in clinical records, nor has it been proven through empirical study.
%R 10.18653/v1/2023.clinicalnlp-1.39
%U https://aclanthology.org/2023.clinicalnlp-1.39
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.39
%P 358-372

Markdown (Informal)

[Intersectionality and Testimonial Injustice in Medical Records](https://aclanthology.org/2023.clinicalnlp-1.39) (Andrews et al., ClinicalNLP 2023)

• Intersectionality and Testimonial Injustice in Medical Records (Andrews et al., ClinicalNLP 2023)

ACL

• Kenya Andrews, Bhuvni Shah, and Lu Cheng. 2023. Intersectionality and Testimonial Injustice in Medical Records. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 358–372, Toronto, Canada. Association for Computational Linguistics.