Evidence map›Paper›PMID 42221510›Full record

ArticleDigital health

Self-administered digital vaccination decision-aids in primary care: A pilot study of COVID-19 vaccination in Atlanta, GA.

Jera White, Tej Shah, Emily Wall, Shivani A Patel, Kevin Wu, Yuxuan Shi, Yuxi Zhang, Ziwei Dong, Ambar Kulshreshtha

Abstract read
In one paragraph

Article in Digital health. 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

9 authors.

Jera WhiteDepartment of Global Health, Emory University Rollins School of Public Health, Atlanta, GA, USA.ORCID https://orcid.org/0009-0004-1475-031X
Tej ShahDepartment of Health Policy and Management, Emory University Rollins School of Public Health, Atlanta, GA, USA.
Emily WallDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Shivani A PatelDepartment of Global Health, Emory University Rollins School of Public Health, Atlanta, GA, USA.
Kevin WuDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Yuxuan ShiDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Yuxi ZhangDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Ziwei DongDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Ambar KulshreshthaDepartment of Family and Preventive Medicine, Emory University School of Medicine, Atlanta, GA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Our objective was to develop and test a digital decision-aid tool to help vaccine-hesitant participants make informed decisions regarding COVID-19 vaccination. Methods: A multidisciplinary team at Emory University developed an online decision-aid tool that presented information about the virus, vaccine risks/benefits, and vaccination resources. This tool was provided to patients in an Atlanta primary care clinic who were not fully vaccinated/boosted. Participants were then surveyed to assess their knowledge and opinions. Results: The tool was administered to 40 patients (55% Black, 70% female, and 45% aged 50 - 69) between January-June2024. Nearly all (97.5%) found the tool useful for improving their understanding of vaccine risks and benefits, with 70% rating it "very" or "extremely" useful. After using the tool, 47.5% of participants reported that the tool had increased their willingness to get vaccinated, and 37.5% indicated they had decided to get vaccinated/boosted. Notably, 22.2% of participants who had never previously received a COVID-19 vaccine stated their intent to get vaccinated after using the tool. Conclusions: This pilot study demonstrated that a digital decision-aid tool can improve knowledge and perceptions surrounding COVID-19 vaccination to encourage healthy decision-making. As such, the tool represents a promising adaptable model for self-administered decision aids in broader healthcare contexts.

Indexed as

COVID-19decision-aiddigital healthmHealthvaccine hesitancyweb-based applications

Identifiers

PMID42221510
PMCPMC13219886

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.