Evidence map›Paper›PMID 41143121›Full record

ArticleTelemedicine reports2025

Explaining Racial/Ethnic Disparities in Telehealth Use with Different Levels of English Proficiency: A Decomposition Approach.

Nari Yoo, Yumin Hong, Yoonyoung Choi

Abstract read
In one paragraph

Article in Telemedicine reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Nari YooSchool of Social Work, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-8467-7048
Yumin HongDepartment of Economics, University of Texas at Austin, Austin, Texas, USA.
Yoonyoung ChoiSchool of Sociology, The University of Arizona, Tucson, Arizona, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study examined disparities in telehealth utilization across various health care services among adults in California, with particular focus on racial, ethnic, and linguistic differences. While telehealth has emerged as a potential tool for addressing health care disparities, evidence suggests that utilization patterns may exacerbate existing inequities. Methods: Using the 2021-2022 California Health Interview Survey (n = 45,893) conducted in diverse languages, we employed descriptive statistics and nonlinear Blinder-Oaxaca decomposition to analyze disparities in telehealth utilization. The Blinder-Oaxaca method systematically partitions outcome differences between groups into explained components and unexplained components. We stratified analyses by visit purpose: mental health, primary care, acute care, chronic care, orthopedics, dermatology, and ophthalmology. Results: Compared with non-Hispanic Whites, African Americans used telehealth 4.1% more ( Discussion: Our findings reveal significant disparities in telehealth utilization associated with race/ethnicity and English proficiency levels, with linguistic barriers most evident in mental health services. The substantial unexplained components in our decomposition analyses suggest that cultural, structural, and linguistic factors beyond socioeconomic factors significantly influence telehealth utilization. These results underscore the need for efforts to develop linguistically appropriate telehealth services, particularly in mental health care. Addressing these barriers is crucial for harnessing telehealth's potential to promote health equity rather than exacerbating existing health disparities in an increasingly digital health care environment.

Indexed as

decompositionhealth disparitieslimited English proficiencyservice usetelehealthtelemental health

Identifiers

PMID41143121
PMCPMC12549173

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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.