Show simple item record

dc.contributor.advisorSiegrist, Richard
dc.contributor.authorLaRose, Emily Anne
dc.date.accessioned2021-11-22T18:44:03Z
dc.date.created2021
dc.date.issued2021-09-14
dc.date.submitted2021
dc.identifier.citationLaRose, Emily Anne. 2021. The COVID-19 Misinfodemic: Using Triple Loop Learning to Guide a Process Evaluation of the COVID-19 Expert Database Project. Doctoral dissertation, Harvard T.H. Chan School of Public Health.
dc.identifier.other28768658
dc.identifier.urihttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37370291*
dc.description.abstractThe COVID-19 pandemic has been a source of overwhelming hardship, grief, and tragedy. It has impacted all of us - how we live, our work, health, and relationships. In addition to the virus-induced health crisis, the world has been inundated by an epidemic of health misinformation – a misinfodemic – fueled by social media and messaging channels online. Health misinformation is not new, but its recognition as a significant public health challenge capable of physical, emotional, and psychological harm has grown during the pandemic. In recent publications, researchers, public health advocates, and others have acknowledged that innovative, cross-sectoral partnerships are vital to curb the spread of health misinformation online. Additionally, there has been a growing acknowledgment that new workflows and resources are needed to support the production of high-quality health- and science-related digital content and counter circulating myths, rumors, and conspiracy theories. In June 2020, Meedan’s Digital Health Lab launched the COVID-19 Expert Database Project as a novel intervention to support journalists and fact-checkers in reporting on pandemic-related health and science topics. The project was designed as a resource where media partners could submit questions to a team of public health experts and receive responses in the form of evidence-based explainers that would also be posted to the project website. This thesis reviews how I planned and executed a process evaluation of the first seven months of the project to determine if it was implemented fully and as intended; evaluate whether the assumptions that underpinned the project were valid; identify which parts of the project worked as planned and which did not; and explore the contextual elements that influenced the project and its implementation. This paper also discusses how I integrated the evaluation into a triple loop learning model that posed the following questions: are we doing our work well (loop one); are we doing the correct work to serve our partners and deliver on our project outputs and outcomes (loop two); and are we gathering information needed to make informed choices about our strategy, objectives, and direction (loop three). Finally, I have highlighted how I applied the evaluation learnings to propose activities for continued monitoring and evaluation efforts in support of ongoing programmatic reflection, learning, and improvement in the coming year. Through qualitative interviews with partners and the project team and a review of program materials, I found that the COVID-19 Expert Database Project was successfully implemented as intended. Between June and December 2020, the project team received pandemic-related questions from organizations representing more than 15 countries and delivered contextually relevant responses to more than 200 questions. In addition, partners universally endorsed the quality, trustworthiness, accessibility, and usefulness of the explainer content and reported routinely using the database alongside resources from the World Health Organization, Centers for Disease Control and Prevention, and other leading health authorities. Despite the overwhelmingly positive feedback provided by partner organizations, the evaluation underscored the need for improved readability and accessibility to meet the project team’s targets in support of health literacy. Additionally, it is essential to acknowledge that the pandemic contributed to the successful launch of the project and its utility. First, data gaps and rapidly emerging scientific findings increased the demand for health-related fact-checking. Second, efforts to combat health misinformation became a global priority in the interest of public safety. Third, the pandemic substantially elevated the perceived importance of addressing health misinformation among journalists and fact-checkers. The COVID-19 Expert Database Project provided a model that successfully supported journalists and fact-checkers in combating health misinformation online. Though the pandemic persists, the project has been relaunched as Health Desk. Now, in addition to pandemic-related content, the project team has begun writing explainers to support fact-checking efforts on other health topics. In the coming year, the team also plans to continue to develop new partnerships and resources to ensure that professional communicators can provide the public with access to accurate, timely, and accessible health information.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectCOVID-19
dc.subjectHealth communications
dc.subjectMisinfodemic
dc.subjectMisinformation
dc.subjectProcess evaluation
dc.subjectTriple loop learning
dc.subjectPublic health
dc.titleThe COVID-19 Misinfodemic: Using Triple Loop Learning to Guide a Process Evaluation of the COVID-19 Expert Database Project
dc.typeThesis or Dissertation
dash.depositing.authorLaRose, Emily Anne
dc.date.available2021-11-22T18:44:03Z
thesis.degree.date2021
thesis.degree.grantorHarvard T.H. Chan School of Public Health
thesis.degree.levelDoctoral
thesis.degree.nameD.P.H.
dc.contributor.committeeMemberBean, William
dc.contributor.committeeMemberArrieta, Jafet
dc.type.materialtext
thesis.degree.departmentPublic Health
dash.author.emaildietitianchef@yahoo.com


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record