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选择完美的镜头——疫苗在线新闻报道中图像的隐含叙事。

Choosing the perfect shot - The loaded narrative of imagery in online news coverage of vaccines.

机构信息

Department of Pediatrics, University of Minnesota Masonic Children's Hospital, Minneapolis, Minnesota, United States of America.

Core Topic Leader of Medical Studies, "Covering Health", Columbia, Missouri, United States of America.

出版信息

PLoS One. 2018 Jun 27;13(6):e0199870. doi: 10.1371/journal.pone.0199870. eCollection 2018.

Abstract

Images in health communication have been shown to affect perspectives and attitudes towards health issues including vaccination. We seek to quantify the frequency of images used in online news coverage of vaccines that may convey varying sentiments about vaccination. To capture a breadth of vaccine-related news coverage, including international sources, we searched the following terms in Google News Archives: "autism and vaccine", "flu and vaccine", and "measles and Disneyland". We developed a coding tool that classified images as negative (eg, screaming child), positive (eg, happy child), neutral (eg, vaccine vial), or irrelevant (eg, picture of journalist). All images were coded independently by two researchers and discussed for consensus. We analyzed 734 images. Of the images which featured vaccines and/or a medical encounter (322), 28% had negative features and 30% had positive features. The remaining 137 images (43%) were neutral. There was no statistically significant difference between proportions of negative and positive imagery for each pair of search terms, which may be a reflection of random image selection. Ultimately, nearly one in eight images included in vaccine-related news coverage contains negative features which may be selected without careful consideration of the potential negative impact on public health initiatives regarding vaccination.

摘要

健康传播中的图像已被证明会影响人们对健康问题的看法和态度,包括疫苗接种。我们旨在量化在线新闻报道中使用的与疫苗相关的图像的频率,这些图像可能会对疫苗接种表达不同的看法。为了涵盖更广泛的疫苗相关新闻报道,包括国际来源,我们在 Google 新闻档案中搜索了以下术语:“自闭症与疫苗”、“流感与疫苗”和“麻疹与迪士尼乐园”。我们开发了一种编码工具,将图像分为负面(例如,尖叫的孩子)、正面(例如,快乐的孩子)、中性(例如,疫苗小瓶)或不相关(例如,记者的照片)。所有图像均由两位研究人员独立编码,并进行了讨论以达成共识。我们分析了 734 张图像。在涉及疫苗和/或医疗接触的图像中(322 张),有 28%的图像具有负面特征,有 30%的图像具有正面特征。其余 137 张图像(43%)为中性。每一对搜索词的负面和正面图像比例之间没有统计学上的显著差异,这可能反映了图像的随机选择。最终,在与疫苗相关的新闻报道中包含的近八分之一的图像包含负面特征,如果不仔细考虑其对疫苗接种公共卫生计划的潜在负面影响,这些特征可能会被选中。

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