Evidence map›Paper›PMID 36409300›Full record

ArticleJMIR human factors2023

Usability and Credibility of a COVID-19 Vaccine Chatbot for Young Adults and Health Workers in the United States: Formative Mixed Methods Study.

Rose Weeks, Pooja Sangha, Lyra Cooper, João Sedoc, Sydney White, Shai Gretz, Assaf Toledo, Dan Lahav, Anna-Maria Hartner, Nina M Martin and 3 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07200570 (Evaluation of a Vaccine Chatbot on HPV Vaccine Confidence and Hesitancy), which is not on this map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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.

NCT07200570 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

Evaluation of a Vaccine Chatbot on HPV Vaccine Confidence and Hesitancy: a Randomized Controlled Trial and Implementation Science Study

TypeinterventionalSponsorFudan UniversityRan2026 to 2026Enrolled1,800ConditionsHPV VaccineArmsLLM-Powered HPV Vaccine Chatbot
3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. The performance of artificial intelligence chatbot large language models to address skeletal biology and bone health queries.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024
    Article
  3. Chatbots and COVID-19: Taking Stock of the Lessons Learned.Journal of medical Internet research · 2024
    Article
  4. Article
  5. Article
  6. ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
    Article
  7. Review
  8. Article
  9. 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

13 authors at 2 institutions in 2 countries.

Rose WeeksInternational Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0003-0171-5043
Pooja SanghaInternational Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-6647-8299
Lyra CooperInternational Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-1496-9854
João SedocStern School of Business, New York University, New York, NY, United States.ORCID https://orcid.org/0000-0001-6369-3711
Sydney WhiteInternational Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-1798-3088
Shai GretzIBM Research AI, Haifa, Israel.ORCID https://orcid.org/0000-0003-4562-7029
Assaf ToledoIBM Research AI, Haifa, Israel.ORCID https://orcid.org/0000-0002-5309-267X
Dan LahavIBM Research AI, Haifa, Israel.ORCID https://orcid.org/0000-0001-6602-8530
Anna-Maria HartnerInternational Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-8502-9915
Nina M MartinDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-3361-7037
Jae Hyoung LeeInternational Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-6303-086X
Noam SlonimIBM Research AI, Haifa, Israel.ORCID https://orcid.org/0000-0001-5171-8264
Naor Bar-ZeevInternational Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0003-0570-4624
Johns Hopkins University · USIBM Research - Haifa · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic raised novel challenges in communicating reliable, continually changing health information to a broad and sometimes skeptical public, particularly around COVID-19 vaccines, which, despite being comprehensively studied, were the subject of viral misinformation. Chatbots are a promising technology to reach and engage populations during the pandemic. To inform and communicate effectively with users, chatbots must be highly usable and credible.

objectiveWe sought to understand how young adults and health workers in the United States assessed the usability and credibility of a web-based chatbot called Vira, created by the Johns Hopkins Bloomberg School of Public Health and IBM Research using natural language processing technology. Using a mixed method approach, we sought to rapidly improve Vira's user experience to support vaccine decision-making during the peak of the COVID-19 pandemic.

methodsWe recruited racially and ethnically diverse young people and health workers, with both groups from urban areas of the United States. We used the validated Chatbot Usability Questionnaire to understand the tool's navigation, precision, and persona. We also conducted 11 interviews with health workers and young people to understand the user experience, whether they perceived the chatbot as confidential and trustworthy, and how they would use the chatbot. We coded and categorized emerging themes to understand the determining factors for participants' assessment of chatbot usability and credibility.

resultsIn all, 58 participants completed a web-based usability questionnaire and 11 completed in-depth interviews. Most questionnaire respondents said the chatbot was "easy to navigate" (51/58, 88%) and "very easy to use" (50/58, 86%), and many (45/58, 78%) said its responses were relevant. The mean Chatbot Usability Questionnaire score was 70.2 (SD 12.1) and scores ranged from 40.6 to 95.3. Interview participants felt the chatbot achieved high usability due to its strong functionality, performance, and perceived confidentiality and that the chatbot could attain high credibility with a redesign of its cartoonish visual persona. Young people said they would use the chatbot to discuss vaccination with hesitant friends or family members, whereas health workers used or anticipated using the chatbot to support community outreach, save time, and stay up to date.

conclusionsThis formative study conducted during the pandemic's peak provided user feedback for an iterative redesign of Vira. Using a mixed method approach provided multidimensional feedback, identifying how the chatbot worked well-being easy to use, answering questions appropriately, and using credible branding-while offering tangible steps to improve the product's visual design. Future studies should evaluate how chatbots support personal health decision-making, particularly in the context of a public health emergency, and whether such outreach tools can reduce staff burnout. Randomized studies should also be conducted to measure how chatbots countering health misinformation affect user knowledge, attitudes, and behavior.

Indexed as

chatbotchatbot developmentconversational agentCOVID-19health informationnatural language processingrisk communicationusabilityuser feedbackvaccine hesitancy

Identifiers

PMID36409300
PMCPMC9947824
OpenAlexW4309517641

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.