Brief Informational Intervention for COVID-19 Misinformation Prophylaxis

COVID19 Behavioral Prophylaxis

misinformation, trust, prevention

As the coronavirus disease 2019 (COVID-19) pandemic has continued to affect life in the United States, the important role of non-pharmaceutical preventive behaviors (such as wearing a face mask) in reducing harm has become clear. In parallel to the pandemic, researchers have observed an infodemic of misinformed or inconsistent narratives about COVID-19. There is growing evidence that misinformed COVID-19 narratives are associated with a wide variety of undesirable behavior (e.g., burning down cell towers). Further, individuals' adherence to recommended COVID-19 preventive guidelines has been inconsistent, and such mandates have engendered opposition and controversy. Recent research suggests the possibility that trust in science and scientists may be an important thread to weave throughout these seemingly disparate components of the modern public health landscape. Thus, this paper describes the protocol for a randomized trial of a brief, digital intervention to increase trust in science. The objective of this trial is to examine if exposure to a curated infographic can increase trust in science, reduce believability of misinformed narratives, and increase likelihood to engage in preventive behaviors.

The investigators propose a single-stage, randomized, superiority trial with 2 parallel groups allocated with a 1:1 ratio. The comparator in this study will be a control (placebo) infographic that is completely unrelated to science (e.g., about cats), but that is developed using the same communication and graphical style. Subjects will be recruited using the Prolific data collection platform, which is one of two primary online crowdsourced research platforms (the other is Amazon's Mechanical Turk, or mTurk). To be included, participants must be identified by Prolific as part of a nationally-representative sample. Participants will also be required to be age 18 or older, and to reside in the United States. Individuals who decline to digitally sign the informed consent document will be excluded and replaced. Per recent best practice recommendations for crowdsourced digital research, attention checks and screens for bots and international users with virtual private networks to mimic US IP addresses will be embedded within the instruments, and failure of more than one attention check, or any bot/location check will result in subject exclusion and replacement. Replacements will be drawn in such a way as to preserve the representativeness of the sample. Missing data will be addressed using either full information maximum likelihood or Markov Chain Monte Carlo multiple imputation strategies. When there is a violation of missing at random (which is unlikely) in preliminary analyses, the investigators will incorporate strategies representing the missingness. The researchers will further explore data quality in terms of outliers, measurement error, non-normality, and variance heterogeneity. Robust methods of analysis (e.g., Huber-White robust standard errors) will be used, as appropriate. For all multi-item measures, reliability will be evaluated prior to computation of the variable.

United States