Approaches, Successes, and Challenges in Recruiting Closed Points of Dispensing Sites: A Qualitative Study.

Health Security(2021)

引用 0|浏览4
摘要
Closed points of dispensing (PODs) are an essential component of local public health preparedness programs because most local public health agencies lack the infrastructure to distribute medical countermeasures to all community members in a short period of time through open PODs alone. However, no study has examined closed POD recruitment strategies or approaches to determine best practices, such as how to select or recruit an agency, group, or business to become a closed POD site once a potential partner has been identified. We conducted qualitative interviews with US disaster planners to identify their approaches and challenges to recruiting closed POD sites. In total, 16 disaster planners participated. Recruitment considerations related to selecting sites, paperwork needed, and challenges faced in recruiting closed POD sites. Important selection criteria for sites included size, agencies or businesses with vulnerable or confined populations who lack access or ability to get to or through open POD sites, and critical infrastructure organizations. Major challenges to recruitment included difficulty convincing sites of closed POD importance, obstacles with recruiting sites that can administer mass vaccination, and fear of legal repercussions related to medical countermeasure dispensing or administration. Closed POD recruitment is a frequently challenging but highly necessary process both before and during the current pandemic. These recommendations can be used by other disaster planners intending to start or expand their closed POD network. Public health agencies should continue working toward improved distribution plans for medical countermeasures, both oral and vaccine, to minimize morbidity and mortality during mass casualty events.
更多
查看译文
关键词
COVID-19,Epidemic management,response,Public health preparedness,response,Points of dispensing,Vaccination,Medical countermeasures
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
0
您的评分 :

暂无评分

数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn