中国医务人员朋友犹豫态度与个人COVID-19疫苗犹豫态度之间的关联
基于中国医务人员调查数据，分析朋友对COVID-19疫苗犹豫态度与个人犹豫态度之间的关联。
基本信息
资源简介
中国医务人员朋友犹豫态度与个人COVID-19疫苗犹豫态度之间的关联
【数据集背景】
本数据集源站标题为《Association between friends’ hesitancy and personal COVID-19 vaccine hesitancy among Chinese medical staff》。配套论文《Association between friends’ hesitancy and personal COVID-19 vaccine hesitancy among Chinese medical staff》发表于 Human Vaccines & Immunotherapeutics（2024），DOI：10.1080/21645515.2024.2344290。项目资助方：The author(s) reported there is no funding associated with the work featured in this article.。源站首次发布：2024-05-08。
【数据内容】
源站原始描述：COVID-19 vaccine hesitancy remains problematic among healthcare workers. Social network influences may shape vaccine decision-making, but few studies have examined this in this critical workforce. We assessed the relationship between friends’ COVID-19 vaccination attitudes and personal hesitancy among Chinese healthcare personnel. In December 2022–January 2023, a cross-sectional online survey was conducted at a tertiary hospital in China using WeChat. Of the 1832 healthcare personnel who were in…
【数据结构与技术规格】
文件数 2 个，合计 498.75 KB。khvi_a_2344290_sm5218.pdf：application/pdf，208.39 KB。khvi_a_2344290_sm5215.pdf：application/pdf，290.36 KB。数据集 DOI：10.6084/m9.figshare.25773061.v1。
【主题与分类】
源站学科分类：Biotechnology、Sociology、Science Policy、Mental Health。主题标签：COVID-19、vaccine hesitancy、medical staff、social networks、China。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/Association_between_friends_hesitancy_and_personal_COVID-19_vaccine_hesitancy_among_Chinese_medical_staff/25773061
使用许可：CC BY-NC-ND 4.0（https://creativecommons.org/licenses/by-nc-nd/4.0/）。
规范引用：Association between friends’ hesitancy and personal COVID-19 vaccine hesitancy among Chinese medical staff. Taylor & Francis. Huang, Lili; Hu, Weiwei; Jiang, Yanhong; Hong, Weiwen
【使用注意】
· 源站最后修改于 2026-01-21，引用时请注意版本时效。
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使用方式
数据集获取（Figshare）
- 源站标题：Association between friends’ hesitancy and personal COVID-19 vaccine hesitancy among Chinese medical staff
- DOI：10.6084/m9.figshare.25773061.v1
- 发布日期：2024-05-08
- 文件数：2 个
- 文件总体积：498.75 KB
- 源站页面：https://tandf.figshare.com/articles/dataset/Association_between_friends_hesitancy_and_personal_COVID-19_vaccine_hesitancy_among_Chinese_medical_staff/25773061
命令行下载
curl -L -o "khvi_a_2344290_sm5218.pdf" "https://ndownloader.figshare.com/files/46178260"
curl -L -o "khvi_a_2344290_sm5215.pdf" "https://ndownloader.figshare.com/files/46178263"
文件清单
khvi_a_2344290_sm5218.pdf（208.39 KB）khvi_a_2344290_sm5215.pdf（290.36 KB）
文件总体积 498.75 KB（源站 API 实测）。
数据说明
COVID-19 vaccine hesitancy remains problematic among healthcare workers. Social network influences may shape vaccine decision-making, but few studies have examined this in this critical workforce. We assessed the relationship between friends’ COVID-19 vaccination attitudes and personal hesitancy among Chinese healthcare personnel. In December 2022–January 2023, a cross-sectional online survey was conducted at a tertiary hospital in China using WeChat. Of the 1832 healthcare personnel who were invited to answer the structured questionnaire, 613 (33.5%) samples had valid data for data analysis. Logistic regression examined the association between friends’ hesitancy and participants’ own hesitancy, adjusting for confounders. Of 613 healthcare workers included, 266 (43.4%) were hesitant. Those with hesitant friends had 6.34 times higher adjusted odds of hesitating themselves versus those without hesitant friends (95% CI 2.97–13.52). Strong associations persisted across subgroups. Chinese healthcare workers’ COVID-19 vaccination hesitancy was highly influenced by perceived friends’ attitudes. Fostering pro-vaccine social norms through trusted peer networks could help promote vaccine acceptance in this critical workforce.
许可
CC BY-NC-ND 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.25773061.v1（source: https://tandf.figshare.com/articles/dataset/Association_between_friends_hesitancy_and_personal_COVID-19_vaccine_hesitancy_among_Chinese_medical_staff/25773061）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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