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Record W4412723076 · doi:10.2196/67288

Community Comfort With Automatic Sharing of Race, Ethnicity, and Language Data Between Health Care Settings: Cross-Sectional Study

2025· article· en· W4412723076 on OpenAlexvenueno aff
Noah Brazer, Baylah Tessier‐Sherman, Deron Galusha, Sakinah C. Suttiratana, Corrine Liu, Katherine Kim, Mark E Abraham, Marcella Nuñez-Smith, Karen Wang

Bibliographic record

VenueInteractive Journal of Medical Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsnot available
FundersU.S. National Library of Medicine
KeywordsPreprintEthnic groupCross-sectional studyRace (biology)Health carePsychologyMedicineComputer scienceSociologyWorld Wide WebPolitical scienceGender studiesAnthropology

Abstract

fetched live from OpenAlex

Background: Little is known regarding patient attitudes toward automatic sharing of race, ethnicity, and language (REL) data in health care settings despite the universal practice of data sharing across health care institutions and providers. Objective: This study aims to assess public comfort with disclosing and automatically sharing REL data in health care settings and understand the social factors associated with these attitudes. Methods: Using the 2022 DataHaven Community Wellbeing Survey from 1196 adult Connecticut residents, we examined factors associated with public comfort with disclosing and automatically sharing REL data across health care settings. We generated unadjusted and adjusted logistic models to examine associations between factors and responses to the data-sharing questions. Results: Most residents surveyed were White (n=873, 73%), followed by African American or Black (n=167, 14%), Asian or Native Hawaiian or other Pacific Islander (n=31, 2.6%), multiracial (n=31, 2.6%), and American Indian or Alaska Native (n=12, 1%). The majority of respondents were not Hispanic or Latino (n=1051, 87.9%). More than half of respondents reported excellent or very good self-rated health (SRH; n=635, 53.1%), and most participants reported almost always trusting their health care provider (n=939, 78.5%). Most participants reported being willing to share race and ethnicity data at a hospital or clinic (n=1008, 84.3%) and REL data automatically (n=947, 79.2%) in health care settings. Hispanic or Latino (adjusted odds ratio [AOR] 0.049, 95% CI 0.25-0.94) and multiracial (AOR 0.32, 95% CI 0.14-0.76) respondents were less likely to be willing to disclose race and ethnicity data compared to those who were not Hispanic or Latino and who were White, respectively. Individuals who sometimes trust health care providers (AOR 0.57, 95% CI 0.35-0.94) or rarely/never (AOR 0.35, 95% CI 0.15-0.85) were less likely to be willing to disclose race and ethnicity data than those who almost always trust health care providers. African American or Black (AOR 0.46, 95% CI 0.29-0.72) and American Indian or Alaska Native (AOR 0.18, 95% CI 0.04-0.75) individuals were less likely to be willing to share REL data automatically than White individuals. Those who sometimes trust health care providers (AOR 0.48, 95% CI 0.31-0.74) or rarely/never trust health care providers (AOR 0.25, 95% CI 0.11-0.56) were less likely to be willing to share REL data automatically than those who almost always trust health care providers. Those with poor/fair SRH versus very good/excellent SRH were less likely to be willing to share REL data automatically (AOR 0.54, 95% CI 0.34-0.85). Conclusions: Racial and ethnic identity, SRH, and trust in health care providers affect willingness to share REL information with providers and other health systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.509
Teacher spread0.432 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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