Evidence map›Paper›PMID 34458683›Full record

ArticleJMIR infodemiology

Examining the Public's Most Frequently Asked Questions Regarding COVID-19 Vaccines Using Search Engine Analytics in the United States: Observational Study.

Nicholas B Sajjadi, Samuel Shepard, Ryan Ottwell, Kelly Murray, Justin Chronister, Micah Hartwell, Matt Vassar

Abstract read
In one paragraph

Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

7 authors.

Nicholas B Sajjadi *Office of Medical Student Research College of Osteopathic Medicine Oklahoma State University Center for Health Sciences Tulsa, OK United States.ORCID https://orcid.org/0000-0003-1716-2404
Samuel Shepard *Office of Medical Student Research College of Osteopathic Medicine Oklahoma State University Center for Health Sciences Tulsa, OK United States.ORCID https://orcid.org/0000-0003-4447-354X
Ryan Ottwell *Department of Internal Medicine University of Oklahoma School of Community Medicine Tulsa, OK United States.ORCID https://orcid.org/0000-0002-6574-2844
Kelly Murray *Department of Emergency Medicine College of Osteopathic Medicine Oklahoma State University Center for Health Sciences Tulsa, OK United States.ORCID https://orcid.org/0000-0002-8090-2156
Justin Chronister *Department of Internal Medicine College of Osteopathic Medicine Oklahoma State University Center for Health Sciences Tulsa, OK United States.ORCID https://orcid.org/0000-0003-2976-826X
Micah Hartwell *Office of Medical Student Research College of Osteopathic Medicine Oklahoma State University Center for Health Sciences Tulsa, OK United States.ORCID https://orcid.org/0000-0001-6810-6571
Matt Vassar *Office of Medical Student Research College of Osteopathic Medicine Oklahoma State University Center for Health Sciences Tulsa, OK United States.ORCID https://orcid.org/0000-0003-2859-6152

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe emergency authorization of COVID-19 vaccines has offered the first means of long-term protection against COVID-19-related illness since the pandemic began. It is important for health care professionals to understand commonly held COVID-19 vaccine concerns and to be equipped with quality information that can be used to assist in medical decision-making.

objectiveUsing Google's RankBrain machine learning algorithm, we sought to characterize the content of the most frequently asked questions (FAQs) about COVID-19 vaccines evidenced by internet searches. Secondarily, we sought to examine the information transparency and quality of sources used by Google to answer FAQs on COVID-19 vaccines.

methodsWe searched COVID-19 vaccine terms on Google and used the "People also ask" box to obtain FAQs generated by Google's machine learning algorithms. FAQs are assigned an "answer" source by Google. We extracted FAQs and answer sources related to COVID-19 vaccines. We used the Rothwell Classification of Questions to categorize questions on the basis of content. We classified answer sources as either academic, commercial, government, media outlet, or medical practice. We used the Journal of the American Medical Association's (JAMA's) benchmark criteria to assess information transparency and Brief DISCERN to assess information quality for answer sources. FAQ and answer source type frequencies were calculated. Chi-square tests were used to determine associations between information transparency by source type. One-way analysis of variance was used to assess differences in mean Brief DISCERN scores by source type.

resultsOur search yielded 28 unique FAQs about COVID-19 vaccines. Most COVID-19 vaccine-related FAQs were seeking factual information (22/28, 78.6%), specifically about safety and efficacy (9/22, 40.9%). The most common source type was media outlets (12/28, 42.9%), followed by government sources (11/28, 39.3%). Nineteen sources met 3 or more JAMA benchmark criteria with government sources as the majority (10/19, 52.6%). JAMA benchmark criteria performance did not significantly differ among source types (

conclusionsThe most frequently asked COVID-19 vaccine-related questions pertained to vaccine safety and efficacy. We found that government sources provided the most transparent and highest-quality web-based COVID-19 vaccine-related information. Recognizing common questions and concerns about COVID-19 vaccines may assist in improving vaccination efforts.

Indexed as

contentCOVID-19frequently asked questionsinternetmachine learningnatural language processingqualityquestionSARS-CoV-2search analyticssearch enginetransparencyvaccinevaccine hesitancyweb-based health information

Identifiers

PMID34458683
PMCPMC8341336

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.