Evidence map›Paper›PMID 40487570›Full record

ArticleHealth equity2025

Ethical Considerations and Recommendations for Humanizing Immigrant Language in Health Equity Data Collection, Reporting, and Measurement.

Andrea Thoumi, Olurotimi Kukoyi, Kamaria Kaalund, Yazmin Garcia Rico, Rosa M Gonzalez-Guarda, Jay Pearson, Viviana Martinez-Bianchi

Abstract read
In one paragraph

Article in Health equity, 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

7 authors.

Andrea ThoumiDepartment of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA.ORCID https://orcid.org/0000-0003-2264-6390
Olurotimi KukoyiUNC-Chapel Hill, Chapel Hill, North Carolina, USA.
Kamaria KaalundJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Yazmin Garcia RicoIndependent Researcher, Greensboro, North Carolina, USA.
Rosa M Gonzalez-GuardaSchool of Nursing, Duke University, Durham, North Carolina, USA.ORCID https://orcid.org/0000-0002-0220-5208
Jay PearsonDuke University Sanford School of Public Policy, Durham, North Carolina, USA.
Viviana Martinez-BianchiFamily Medicine & Community Health, Duke University, Durham, North Carolina, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Collecting accurate and consistent sociodemographic data is needed to improve health measurement and public health interventions. Missing or inaccurate data hinders the adequate assessment of the state of access, quality, and coverage in the overall population and communities experiencing social marginalization. Health measurement requires data labels that humanize all populations living, working, and residing across the United States and territories. Humanization is fundamentally grounded in the concepts of human dignity and ethical identity integrity. An often-overlooked form of exclusion in health care is the long-standing use of dehumanizing language, including its use in health measurement and data collection efforts, to refer to immigrant populations. In this perspective, we delineate ethical concerns regarding the use of dehumanizing language when referring to immigrant populations. We provide recommendations for health providers, researchers, and policy makers in improving humanizing language in health equity data collection and reporting through engagement of community experts, use of alternative language, implementation, and monitoring.

Indexed as

data equityhealth disparitieshealth equityimmigrant healthLatinx healthpopulation health

Identifiers

PMID40487570
PMCPMC12143360

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.