在流行病期间影响印第安纳州拉斐特的社区福祉

Eli Coltin, Eric Flaningam, Jace T Newell, Jason Ware
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摘要

在过去的五年里,杰森·韦尔博士以社区为中心的研究和服务学习课程,围绕当地社区合作伙伴的需求,因为他们共同关注社区福祉问题。他们的工作本质上优先考虑了定性研究方法,如通过深入访谈的叙事调查和通过在不同的服务提供机构(如哈特福德中心和汉纳社区中心)进行沉浸式观察的民族志。COVID-19及其持续传播的威胁意味着,韦尔博士、他的学生和他们的社区合作伙伴必须以不同的方式开展工作。他们的回应是一个支点。他们转向挖掘大型的可公开访问和专有的数据集,如美国人口普查数据、住房抵押贷款披露法案(HMDA)数据、无家可归者管理信息系统(HMIS)数据和Polk目录数据。该中心直接回应了拉斐特市对有用数据的需求,这些数据可以为社区振兴、经济适用房和无家可归者干预相关的决策提供信息。这种不同的方法影响了共同作者的学习和学术发展,并为社区合作伙伴提供了有用的数据。共同作者在追求数据特定问题、提取数据、分析和可视化方面经历了更大的自主权。其中一位合著者自学了Python来导入、统计分析和可视化数据,然后将研究结果提交给拉斐特市。合著者的初步工作——一项试点研究——导致了一个规模扩大的项目,为三个不同的社区合作伙伴带来了五项重要产出,直接影响了拉斐特北端的六个社区。另一位合著者在大流行期间专注于学术研究,他领导了一项全面的文献综述,重点关注以社区为基础的机器人项目对少数民族青年的影响。在撰写多篇出版物的同时,作者的演讲也被地方、国家和国际各级所接受。第三位合著者正在与其他作者合作创建一个自动化系统,该系统将支持二手数据的收集、提取和分析,从而促进未来可持续的数据分析。
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Impacting Community Well-Being in Lafayette, Indiana, in the Midst of a Pandemic
For the past five years, Dr. Jason Ware has centered community-based research and service-learning courses around local community partners’ needs as they focused collectively on community well-being issues. The nature of their work has prioritized qualitative research methods such as narrative inquiry via in-depth interviews and ethnography via immersive observations within varying service-providing institutions such as the Hartford Hub and the Hanna Community Center. COVID-19 and the constant threat of its transmission meant that Dr. Ware, his students, and their community partners had to approach their work differently. They responded with a pivot. They turned to mining large publicly accessible and proprietary data sets, such as United States Census data, Home Mortgage Disclosure Act (HMDA) data, the Homeless Management Information System (HMIS) data, and the Polk Directory data. The pivot served as a direct response to the City of Lafayette’s need for useful data that could inform decision-making related to neighborhood revitalization, affordable housing, and homelessness intervention. This different approach impacted the co-authors’ learning and scholarly development and provided the community partners with useful data. The co-authors experienced increased autonomy in pursuing data-specific questions, extracting data, analyzing, and visualizing it. One of the co-authors taught himself Python to import, statistically analyze, and visualize the data, and then presented the findings to the City of Lafayette. The co-author’s initial work — a pilot study — led to a scaled-up project that resulted in five significant outputs for three different community partners with a direct impact on six neighborhoods in the north end of Lafayette. Another co-author, who focused on scholarship during the pandemic, led an effort to develop a comprehensive literature review focused on the effect of community-based robotics programs on minority youth. The co-author also had presentations accepted at the local, national, and international levels while working on multiple publications. The third co-author is partnering with the other authors to create an automated system that will support the collection, extraction, and analysis of secondary data that will facilitate sustainable data analysis into the future.
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