@文章{信息:doi/10.2196/13694,作者=“朱承燕曾艳艳张润喜魏伟Evans Richard何蓉蓉”,标题=“社交媒体时代准妈妈妊娠相关信息的寻求与分享:定性研究”,期刊=“J医学互联网研究”,年=“2019”,月=“12”,日=“4”,卷=“21”,数=“12”,页=“e13694”,关键词=“孕妇;社交媒体;信息寻求;消费者健康信息;背景:社交媒体已经成为包括准妈妈在内的中国公民最常用的交流工具。越来越多的女性在怀孕期间通过互动网站、即时通讯和手机应用等各种形式的社交媒体渠道来解决问题和获得问题的答案。尽管全球范围内对孕妇使用互联网的情况进行了广泛研究,但在2015年结束了一胎政策的中国,探索社交媒体使用习惯变化的研究有限。目的:本研究旨在(1)了解中国孕妇通过社交媒体寻求和分享怀孕相关信息的现状,(2)揭示社交媒体使用的影响,(3)揭示通过社交媒体渠道提供的怀孕相关卫生服务。方法:采用定性方法来研究社交媒体的使用及其对孕妇的影响。2017年7月20日至8月10日,共采访了20名怀孕且处于不同怀孕阶段的女性。 Thematic analysis was conducted on the collected data to identify patterns in usage. Results: Overall, 80{\%} (16/20) of participants were aged in their 20s (mean 28.5 years [SD 4.3]). All had used social media for pregnancy-related purposes. For the seeking behavior, 18 codes were merged into 4 themes, namely, gravida, fetus, delivery, and the postpartum period; whereas for sharing behaviors, 10 codes were merged into 4 themes, namely, gravida, fetus, delivery, and caretaker. Lurking, small group sharing, bad news avoidance, and cross-checking were identified as the preferred patterns for using social media. Overall, 95{\%} (19/20) of participants reported a positive mental impact from using social media during their pregnancy. Conclusions: It is indisputable that social media has played an increasingly important role in supporting expectant mothers in China. The specific seeking and sharing patterns identified in this study indicate that the general quality of pregnancy-related information on social media, as well as Chinese culture toward pregnancy, is improving. The new themes that merge in pregnancy-related social media use represent a shift toward safe pregnancy and the promotion of a more enjoyable pregnancy. Future prenatal care should provide further information on services related to being comfortable during pregnancy and reducing the inequality of social media--based services caused by the digital divide. ", issn="1438-8871", doi="10.2196/13694", url="//www.mybigtv.com/2019/12/e13694", url="https://doi.org/10.2196/13694", url="http://www.ncbi.nlm.nih.gov/pubmed/31799939" }
Baidu
map