Evidence map›Paper›PMID 40844317›Full record

ArticleJournal of medical Internet research2025

The All of Us Research Program's Social Media Outreach to Underrepresented Populations: Mixed Methods Analysis.

Dennis J Zhang, Michael G Bentz, Janet K Shim, Sandra S-J Lee

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Dennis J ZhangDepartment of Biological Sciences, Columbia University, 1212 Amsterdam Ave, New York, NY, 10027, United States.ORCID 0000-0002-6871-9842
Michael G BentzDepartment of Medical Humanities and Ethics, Columbia University, New York, NY, United States.ORCID 0000-0002-0475-2563
Janet K Shim *Department of Social & Behavioral Sciences, University of California, San Francisco, San Francisco, CA, United States.ORCID 0000-0002-4597-7961
Sandra S-J Lee *Department of Medical Humanities and Ethics, Columbia University, New York, NY, United States.ORCID 0000-0002-2312-9814

Funding

Understanding the Expectations and Needs of Diverse Communities for Navigating Research ResultsR01HG010330 · NHGRI · STANFORD UNIVERSITY · PI LEE, SANDRA SOO-JIN, SHIM, JANET K · 2018 to 2021
$2.9M
NHGRI NIH HHS R01 HG010330
6 · The paper itself

Abstract

Background: The All of Us Research Program (AoURP) is a prominent precision medicine research initiative committed to diverse participation. The program harnesses digital outreach as a key strategy for recruiting and retaining underrepresented populations, using language that sometimes invokes notions of solidarity. This targeted recruitment of underrepresented groups and potential use of solidaristic language raise concerns about how participation will manifest tangible benefits for these populations and whether institutions assume responsibility for addressing past and present research harms. Objective: This study examines how the AoURP conceptualizes "diversity" in its social media outreach and how this implementation aligns with the program's stated goals. Specifically, we perform a mixed methods analysis to descriptively capture (1) which underrepresented populations are targeted by the AoURP's social media and (2) how solidaristic messaging is used, if at all, in these calls for participation. Methods: AoURP social media posts (n=380) from a 6-month period in 2020-2021 were coded to identify visual depictions and explicit mentions of any "underrepresented in biomedical research" (UBR) categories officially targeted by the program. To then characterize UBR-specific appeals, we performed a thematic analysis of UBR-targeted posts, using a coding scheme that identified unsolidaristic language (ie, appeals to individual benefits) and solidaristic language (ie, appeals to benefitting others, attaining shared goals, and addressing injustices). Results: Among the 10 UBR categories officially recognized by the AoURP, "Race and Ethnicity" (187/380, 49% of posts) and "Age" (71/380, 19%) were the most frequently emphasized, while each of the other remaining categories was rarely invoked (<4/380, 1%). The thematic analysis further identified calls to participate that spanned receiving genetic results (ie, individual benefits), uncovering family and community disease predispositions (ie, benefitting others), improving the future of health (ie, achieving shared goals), and addressing data and health disparities (ie, resolving injustices). Conclusions: In addition to highlighting UBR categories that are more and less emphasized in the AoURP's social media outreach, we also find that the program's messaging indeed resembles a solidaristic appeal to participate. Drawing upon the existing literature on solidarity, we leverage conceptualizations of solidarity as a shared practice grounded in mutuality and bidirectionality to question the AoURP's appeals when institutions fail to fully reciprocate this solidarity. Specifically, we raise concerns about (1) unclear links between participation and addressing health disparities, (2) incomplete acknowledgment of institutions' role in data disparities, and (3) the use of empowerment rhetoric that diverts the onus for correcting these disparities onto participants. Finally, we consider the implications of these issues for future outreach efforts.

Indexed as

Biomedical ResearchMinority GroupsSocial MediaCommunity-Institutional RelationsCultural DiversityHumansPrecision MedicineUnited Statesdata disparitiesdigital outreachhealth disparitiesprecision medicineresearch recruitmentsocial mediasolidarityunderrepresented in biomedical research

Identifiers

PMID40844317
PMCPMC12372410

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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.