Evidence map›Paper›PMID 37983232›Full record

ArticlePloS one2023

Adults' willingness to report sexual orientation and gender identity when registering for a digital health application: A cross-sectional quantitative study.

Jaclyn Marshall, Xinyu Zhang, Benjamin B Green

Abstract read
In one paragraph

Article in PloS one, 2023. 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.

Jaclyn MarshallIncluded Health, San Francisco, CA, United States of America.ORCID 0000-0002-3451-2624
Xinyu ZhangIncluded Health, San Francisco, CA, United States of America.
Benjamin B GreenIncluded Health, San Francisco, CA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The collection of patient sexual orientation and gender identity information is crucial in identifying and addressing disparities in healthcare access, quality, and outcomes for sexual and gender minority individuals. While some studies have explored patients' willingness to disclose this information in specific settings, little is known about response rates in digital health applications. In light of the growing use of digital health, including virtual care, we sought to determine whether adults would respond to optional sexual orientation and gender identity fields during registration for a digital health application offered through their employer-provided benefits. We analyzed response rates for sexual orientation and gender identity by age, race and ethnicity, and region among individuals over age 17 between September 9th and December 31, 2022. Our study, which included over 41,000 commercially-insured adults from all 50 states, found that nearly 80% were willing to report their sexual orientation and gender identity. However, we observed higher nonresponse rates among older adults and individuals living in central and southern regions, with no consistent pattern by race and ethnicity. Our findings indicate that digital health applications could be a valuable resource for collecting this data from a diverse group of adults. Nevertheless, digital health companies must ensure that they use the data responsibly, identifying quality improvement initiatives and contributing to research that can inform health policies for sexual and gender minority individuals.

Indexed as

Gender IdentitySexual and Gender MinoritiesAdolescentAgedCross-Sectional StudiesEthnicityFemaleHumansMaleSexual Behavior

Identifiers

PMID37983232
PMCPMC10659155

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.