Evidence map›Paper›PMID 38734158›Full record

ArticleJournal of biomedical informatics2024

Opportunities for incorporating intersectionality into biomedical informatics.

Oliver J Bear Don't Walk, Amandalynne Paullada, Avery Everhart, Reggie Casanova-Perez, Trevor Cohen, Tiffany Veinot

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Oliver J Bear Don't WalkDepartment of Biomedical Informatics and Medical Education, University of Washington, United States. Electronic address: obdw4@uw.edu.
Amandalynne PaulladaDepartment of Biomedical Informatics and Medical Education, University of Washington, United States.
Avery EverhartDepartment of Geography, Faculty of Arts, University of British Columbia, Canada.
Reggie Casanova-PerezDepartment of Biomedical Informatics and Medical Education, University of Washington, United States.
Trevor CohenDepartment of Biomedical Informatics and Medical Education, University of Washington, United States.
Tiffany VeinotSchool of Information and School of Public Health, University of Michigan, United States.

Funding

AIM-AHEAD Coordinating Center - All Four CoresOT2OD032581 · OD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Paul Avillach, Bettina M. Beech · 2021 to 2026
$168.7M
University of Washington Biomedical Informatics and Data Science Research Training ProgramT15LM007442 · NLM · UNIVERSITY OF WASHINGTON · PI JOHN H. GENNARI, Andrea L. Hartzler · 2002 to 2026
$19.6M
Pharmacogenomic approaches to drug metabolism in American Indian/Alaska Native PeopleR35HG011319 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI CLAW, KATRINA G · 2020 to 2024
$2.9M
Collaboratively Identifying Population-Specific Social Determinants of Health for Indigenous Patients Living with HIV: From Patient Perspectives to the Electronic Health RecordK99LM014579 · NLM · UNIVERSITY OF WASHINGTON · PI BEAR DON'T WALK, OLIVER JOHN · 2024 to 2025
$176k
NHGRI NIH HHS R35 HG011319NIH HHS OT2 OD032581NLM NIH HHS K99 LM014579NLM NIH HHS T15 LM007442
6 · The paper itself

Abstract

Many approaches in biomedical informatics (BMI) rely on the ability to define, gather, and manipulate biomedical data to support health through a cyclical research-practice lifecycle. Researchers within this field are often fortunate to work closely with healthcare and public health systems to influence data generation and capture and have access to a vast amount of biomedical data. Many informaticists also have the expertise to engage with stakeholders, develop new methods and applications, and influence policy. However, research and policy that explicitly seeks to address the systemic drivers of health would more effectively support health. Intersectionality is a theoretical framework that can facilitate such research. It holds that individual human experiences reflect larger socio-structural level systems of privilege and oppression, and cannot be truly understood if these systems are examined in isolation. Intersectionality explicitly accounts for the interrelated nature of systems of privilege and oppression, providing a lens through which to examine and challenge inequities. In this paper, we propose intersectionality as an intervention into how we conduct BMI research. We begin by discussing intersectionality's history and core principles as they apply to BMI. We then elaborate on the potential for intersectionality to stimulate BMI research. Specifically, we posit that our efforts in BMI to improve health should address intersectionality's five key considerations: (1) systems of privilege and oppression that shape health; (2) the interrelated nature of upstream health drivers; (3) the nuances of health outcomes within groups; (4) the problematic and power-laden nature of categories that we assign to people in research and in society; and (5) research to inform and support social change.

Indexed as

Medical InformaticsBiomedical ResearchHumansBiomedical InformaticsIntersectionalityMixed-methods researchQualitative researchQuantitative researchSystems of privilege and oppression

Identifiers

PMID38734158
PMCPMC11146624

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.