Evidence map›Paper›PMID 41393808›Full record

Article... International web for all conference. Web for All Conference2025

Joint Bilingual Navigation of Informed Consent and Intake Forms in American Sign Language and Written English.

Matthew Seita, Michaela Usha, Rachel Skwersky, Poorna Kushalnagar, Raja Kushalnagar

Abstract read
In one paragraph

Article in ... International web for all conference. Web for All Conference, 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

5 authors.

Matthew SeitaGallaudet University, Washington, DC, USA.
Michaela UshaGallaudet University, Washington, DC, USA.
Rachel SkwerskyUniversity of California at Berkeley, Berkeley, CA, USA.
Poorna KushalnagarGallaudet University, Washignton, DC, USA.
Raja KushalnagarGallaudet University, Washington, DC, USA.

Funding

CONSENT-ASL Toolkit for Deaf and Hard of Hearing PeopleG08LM013797 · NLM · GALLAUDET UNIVERSITY · PI KUSHALNAGAR, RAJA S. · 2021 to 2023
$420k
NLM NIH HHS G08 LM013797
6 · The paper itself

Abstract

People who are Deaf or Hard of Hearing (DHH) experience significant health disparities primarily due to barriers in communication accessibility. Approximately 500,000 individuals in the United States primarily use American Sign Language (ASL) and frequently encounter difficulties with informed consent or intake forms that are presented exclusively in written English rather than their native language. Recent advancements in machine learning for Individual Sign Language Recognition (ISLR)-the translation of individual ASL signs-provide an opportunity to develop fully bilingual forms in both ASL and written English. These bilingual forms have significant potential in healthcare and employment contexts, ensuring comprehensive accessibility for Deaf signers fluent in ASL and hearing intake personnel fluent in English. We propose the concept of "joint bilingual navigation," which allows users to interact with digital forms either by signing into a camera-equipped device or through touchscreen interaction with written English. In this paper, we evaluate a bilingual informed consent and intake form that simultaneously supports navigation via natural ASL signs and traditional touchscreen input. Our user studies assessed interest and usability among Deaf signers and hearing non-signing intake staff, particularly focusing on obtaining informed consent and signatures from Deaf participants. The results indicate that such bilingual approaches are effective and positively received. These findings have broader implications, demonstrating that similar bilingual navigation techniques can be applied effectively in other domains requiring inclusive and accessible interactions.

Indexed as

American Sign LanguageDeaf and Hard of HearingInformed ConsentMachine LearningSign Language Recognition

Identifiers

PMID41393808
PMCPMC12701810

What OpenQuestion holds

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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.