Sajjadi Nicholas B, Shepard Samuel, Ottwell Ryan, Murray Kelly, Chronister Justin, Hartwell Micah, Vassar Matt
Office of Medical Student Research College of Osteopathic Medicine Oklahoma State University Center for Health Sciences Tulsa, OK United States.
Department of Internal Medicine University of Oklahoma School of Community Medicine Tulsa, OK United States.
JMIR Infodemiology. 2021 Aug 4;1(1):e28740. doi: 10.2196/28740. eCollection 2021 Jan-Dec.
The 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.
Using 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.
We 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.
Our 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 ( =7.40; =.12). One-way analysis of variance revealed a significant difference in mean Brief DISCERN scores by source type ( =10.27; <.001).
The 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.
自疫情开始以来，新冠病毒疫苗的紧急授权提供了首个长期预防新冠相关疾病的手段。对于医疗保健专业人员来说，了解人们普遍对新冠病毒疫苗存在的担忧，并掌握可用于协助医疗决策的高质量信息非常重要。
利用谷歌的RankBrain机器学习算法，我们试图描述互联网搜索中关于新冠病毒疫苗最常见问题（常见问题解答）的内容特征。其次，我们试图研究谷歌用于回答新冠病毒疫苗常见问题解答的信息来源的透明度和质量。
我们在谷歌上搜索新冠病毒疫苗相关词汇，并使用“人们也问”框获取由谷歌机器学习算法生成的常见问题解答。谷歌会为常见问题解答分配一个“答案”来源。我们提取了与新冠病毒疫苗相关的常见问题解答和答案来源。我们使用罗斯韦尔问题分类法根据内容对问题进行分类。我们将答案来源分为学术、商业、政府、媒体或医疗实践。我们使用美国医学会杂志（JAMA）的基准标准来评估信息透明度，并使用简要辨别工具来评估答案来源的信息质量。计算常见问题解答和答案来源类型的频率。使用卡方检验来确定信息透明度与来源类型之间的关联。使用单因素方差分析来评估不同来源类型的简要辨别平均得分的差异。
我们的搜索产生了28个关于新冠病毒疫苗的独特常见问题解答。大多数与新冠病毒疫苗相关的常见问题解答是在寻求事实性信息（22/28，78.6%），特别是关于安全性和有效性（9/22，40.9%）。最常见的来源类型是媒体（12/28，42.9%），其次是政府来源（11/28，39.3%）。19个来源符合3项或更多JAMA基准标准，其中政府来源占多数（10/19，52.6%）。不同来源类型在JAMA基准标准的表现上没有显著差异（=7.40；=.12）。单因素方差分析显示，不同来源类型的简要辨别平均得分存在显著差异（=10.27；<.001）。
最常被问到的与新冠病毒疫苗相关的问题涉及疫苗的安全性和有效性。我们发现政府来源提供了最透明、质量最高的基于网络的新冠病毒疫苗相关信息。认识到关于新冠病毒疫苗的常见问题和担忧可能有助于改进疫苗接种工作。
