Nagpal Sajan Jiv Singh, Karimianpour Ahmadreza, Mukhija Dhruvika, Mohan Diwakar, Brateanu Andrei
Department of Internal Medicine, Cleveland Clinic Foundation, 9500 Euclid Ave, NA-10, Cleveland, OH 44195 USA.
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA.
Springerplus. 2015 Aug 28;4:457. doi: 10.1186/s40064-015-1251-9. eCollection 2015.
The content and quality of medical information available on video sharing websites such as YouTube is not known. We analyzed the source and quality of medical information about Ebola hemorrhagic fever (EHF) disseminated on YouTube and the video characteristics that influence viewer behavior. An inquiry for the search term 'Ebola' was made on YouTube. The first 100 results were arranged in decreasing order of "relevance" using the default YouTube algorithm. Videos 1-50 and 51-100 were allocated to a high relevance (HR), and a low relevance (LR) video group, respectively. Multivariable logistic regression models were used to assess the predictors of a video being included in the HR vs. LR groups. Fourteen videos were excluded because they were parodies, songs or stand-up comedies (n = 11), not in English (n = 2) or a remaining part of a previous video (n = 1). Two scales, the video information and quality and index and the medical information and content index (MICI) assessed the overall quality, and the medical content of the videos, respectively. There were no videos from hospitals or academic medical centers. Videos in the HR group had a higher median number of views (186,705 vs. 43,796, p < 0.001), more 'likes' (1119 vs. 224, p < 0.001), channel subscriptions (208 vs. 32, p < 0.001), and 'shares' (519 vs. 98, p < 0.001). Multivariable logistic regression showed that only the 'clinical symptoms' component of the MICI scale was associated with a higher likelihood of a video being included in the HR vs. LR group.(OR 1.86, 95 % CI 1.06-3.28, p = 0.03). YouTube videos presenting clinical symptoms of infectious diseases during epidemics are more likely to be included in the HR group and influence viewers behavior.
在YouTube等视频分享网站上可获取的医学信息的内容和质量尚不清楚。我们分析了在YouTube上传播的关于埃博拉出血热(EHF)的医学信息的来源和质量,以及影响观众行为的视频特征。在YouTube上对搜索词“埃博拉”进行了查询。使用默认的YouTube算法,前100个结果按“相关性”降序排列。视频1 - 50和51 - 100分别被分配到高相关性(HR)和低相关性(LR)视频组。多变量逻辑回归模型用于评估视频被纳入HR组与LR组的预测因素。14个视频被排除,因为它们是恶搞、歌曲或单口喜剧(n = 11)、非英语(n = 2)或前一个视频的剩余部分(n = 1)。两个量表,视频信息与质量指数以及医学信息与内容指数(MICI)分别评估了视频的整体质量和医学内容。没有来自医院或学术医学中心的视频。HR组的视频中位数观看次数更高(186,705次对43,796次,p < 0.001),“点赞”数更多(1119次对224次,p < 0.001),频道订阅数更多(208次对32次,p < 0.001),以及“分享”数更多(519次对98次,p < 0.001)。多变量逻辑回归显示,只有MICI量表的“临床症状”部分与视频被纳入HR组而非LR组的可能性更高相关。(比值比1.86,95%置信区间1.06 - 3.28,p = 0.03)。在疫情期间展示传染病临床症状的YouTube视频更有可能被纳入HR组并影响观众行为。