Evidence map›Paper›PMID 37802041›Full record

ArticleAmerican journal of human genetics2023

The need for an intersectionality framework in precision medicine research.

Maya Sabatello, Gregory Diggs-Yang, Alicia Santiago, Carla Easter, Kim Jacoby Morris, Brittany M Hollister, Michael Hahn, Kellan Baker, Alma McCormick, Ella Greene-Moton and 2 more

Open access · bronzeAbstract read
In one paragraph

Article in American journal of human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
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

10 citing papers in PubMed, 12 citations in OpenAlex.

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  10. Intersectionality,Cancers · 2024
    Article
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

12 authors at 10 institutions in 2 countries.

Maya SabatelloCenter for Precision Medicine and Genomics, Department of Medicine, New York, NY, USA; Division of Ethics, Department of Medical Humanities and Ethics, New York, NY, USA. Electronic address: ms4075@cumc.columbia.edu.
Gregory Diggs-YangStacey Nicholas Office of Access and Inclusion, University of California Irvine, Irvine, CA, USA.
Alicia SantiagoDivision of Research on Learning, STEM Education Directorate, National Science Foundation, Alexandria, VA, USA.
Carla EasterSmithsonian National Museum of Natural History, Washington, DC, USA.
Kim Jacoby MorrisAir Force Office of Scientific Research, Air Force Research Laboratory, Arlington, VA, USA.
Brittany M HollisterUniversity of Florida Genetics Institute, University of Florida, Gainesville, FL, USA.
Michael HahnAll of Us Research Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA.
Kellan BakerWhitman-Walker Institute, Washington, DC, USA.
Alma McCormickMessengers for Health, Crow Nation, Montana.
Ella Greene-MotonCommunity-Based Organization Partners (CBOP) and Community Ethics Review Board (CERB), Flint, MI, USA.
Christina DaultonTraining, Diversity, and Health Equity Office, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Greta GotoAlaska Center for Climate Assessment and Policy, University of Alaska Fairbanks, Anchorage, AK, USA.
National Institutes of Health · USCal Humanities · USCommunity Partners · USNational Museum of Natural History · USUnited States Air Force Office of Scientific Research · USUniversity of Alaska Fairbanks · USUniversity of California, Irvine · USUniversity of Florida · USU.S. National Science Foundation · USWhitman-Walker Health · US

Funding

Disability, diversity and trust in precision medicine research: stakeholdersengagementR01HG010868 · NHGRI · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI SABATELLO, MAYA · 2020 to 2023
$3.5M
NHGRI NIH HHS R01 HG010868
6 · The paper itself

Abstract

Precision medicine research has seen growing efforts to increase participation of communities that have been historically underrepresented in biomedical research. Marginalized racial and ethnic communities have received particular attention, toward the goal of improving the generalizability of scientific knowledge and promoting health equity. Against this backdrop, research has highlighted three key issues that could impede the promise of precision medicine research: issues surrounding (dis)trust and representation, challenges in translational efforts to improve health outcomes, and the need for responsive community engagement. Existing efforts to address these challenges have predominantly centered on single-dimensional demographic criteria such as race, ethnicity, or sex, while overlooking how these and additional variables, such as disability, gender identity, and socioeconomic factors, can confound and jointly impact research participation. We argue that increasing cohort diversity and the responsiveness of precision medicine research studies to community needs requires an approach that transcends conventional boundaries and embraces a more nuanced, multi-layered, and intersectional framework for data collection, analyses, and implementation. We draw attention to gaps in existing work, highlight how overlapping layers of marginalization might shape and substantiate one another and affect the precision-medicine research cycle, and put forth strategies to facilitate equitable advantages from precision-medicine research to diverse participants and internally heterogeneous communities.

Indexed as

Biomedical ResearchIntersectional FrameworkEthnicityFemaleGender IdentityHumansMalePrecision Medicinecommunity engagement(dis)trustintersectionalityprecision medicine researchtranslational genomics

Identifiers

PMID37802041
PMCPMC10577071
OpenAlexW4387377568

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.