Our Voice NOLA: Leveraging a Community Engaged Citizen Science Method to Contextualize the New Orleans Food Environment

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH(2022)

引用 1|浏览1
暂无评分
摘要
Objective: We employed the Our Voice citizen scientist method using a mobile application (app) to identify and contextualize neighborhood-level features influencing food access and wellbeing in New Orleans, Louisiana. Design: A three-phase, multi-method study comprised of: (1) a researcher-assisted tag-a-long neighborhood walk (referred to as a 'journey') with the Discovery Tool (DT) app to document neighborhood-level features via geo-coded photos and audio-recorded narratives; (2) a post-journey interview to enable citizen scientists to share their lived experiences; and (3) a community meeting with citizen scientists and local stakeholders. Setting: Various neighborhoods in New Orleans, Louisiana, USA. Participants: Citizen Scientists (i.e., residents) aged 18 years and older. Main Outcome Measure(s): Features that influence food access and health behaviors. Analysis: Descriptive statistics and a thematic content analysis were conducted to assess survey and app data. Results: Citizen scientists (N = 14) captured 178 photos and 184 audio narratives. Eight major themes were identified: safety; walkability; aesthetics; amenities; food; health services; neighborhood changes; and infrastructure/city planning. The post-journey interview provided insights around the abovementioned themes. The community meeting demonstrated the willingness of citizen scientists and stakeholders to convene and discuss issues and relevant solutions. Conclusions and Implications: Findings demonstrate the ability of technology and citizen science to help better understand the complexities of New Orleans' past, present and distinct culture-and implications for food access and wellbeing in the context of trauma in an urban ecosystem.
更多
查看译文
关键词
citizen science,food access,digital health,food environment,social determinants of health
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要