Evidence map›Paper›PMID 36719815›Full record

ArticleJMIR formative research2023

Voice Assistants' Responses to Questions About the COVID-19 Vaccine: National Cross-sectional Study.

Philip Sossenheimer, Grace Hong, Anna Devon-Sand, Steven Lin

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2023. 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
0.6field-weighted citation impact, top 29% 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.

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, 1 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Philip SossenheimerDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.ORCID https://orcid.org/0000-0001-6962-7539
Grace HongDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.ORCID https://orcid.org/0000-0001-7866-290X
Anna Devon-SandDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.ORCID https://orcid.org/0000-0002-8151-3048
Steven LinDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.ORCID https://orcid.org/0000-0001-8108-053X
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence-powered voice assistants (VAs), such as Apple Siri, Google Assistant, and Amazon Alexa, interact with users in natural language and are capable of responding to simple commands, searching the internet, and answering questions. Despite being an increasingly popular way for the public to access health information, VAs could be a source of ambiguous or potentially biased information.

objectiveIn response to the ongoing prevalence of vaccine misinformation and disinformation, this study aims to evaluate how smartphone VAs respond to information- and recommendation-seeking inquiries regarding the COVID-19 vaccine.

methodsA national cross-sectional survey of English-speaking adults who owned a smartphone with a VA installed was conducted online from April 22 to 28, 2021. The primary outcomes were the VAs' responses to 2 questions: "Should I get the COVID vaccine?" and "Is the COVID vaccine safe?" Directed content analysis was used to assign a negative, neutral, or positive connotation to each response and website title provided by the VAs. Statistical significance was assessed using the t test (parametric) or Mann-Whitney U (nonparametric) test for continuous variables and the chi-square or Fisher exact test for categorical variables.

resultsOf the 466 survey respondents included in the final analysis, 404 (86.7%) used Apple Siri, 53 (11.4%) used Google Assistant, and 9 (1.9%) used Amazon Alexa. In response to the question "Is the COVID vaccine safe?" 419 (89.9%) users received a direct response, of which 408 (97.3%) had a positive connotation encouraging users to get vaccinated. Of the websites presented, only 5.3% (11/207) had a positive connotation and 94.7% (196/207) had a neutral connotation. In response to the question "Should I get the COVID vaccine?" 93.1% (434/466) of users received a list of websites, of which 91.5% (1155/1262) had a neutral connotation. For both COVID-19 vaccine-related questions, there was no association between the connotation of a response and the age, gender, zip code, race or ethnicity, and education level of the respondent.

conclusionsOur study found that VAs were much more likely to respond directly with positive connotations to the question "Is the COVID vaccine safe?" but not respond directly and provide a list of websites with neutral connotations to the question "Should I get the COVID vaccine?" To our knowledge, this is the first study to evaluate how VAs respond to both information- and recommendation-seeking inquiries regarding the COVID-19 vaccine. These findings add to our growing understanding of both the opportunities and pitfalls of VAs in supporting public health information dissemination.

Indexed as

artificial intelligenceCOVID-19informationmHealthmisinformationmobile phoneonlinepublic healthsmartphoneusersvaccinationvaccination hesitancy

Identifiers

PMID36719815
PMCPMC9947766
OpenAlexW4318669789

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.