ABSTRACT
BACKGROUND: Previous research has indicated that demographic differences affect COVID-19 vaccination rates. Trust, in both the vaccine itself and institutional trust, is one possible factor. The present study examines racial differences in institutional trust and vaccine status among a nationally representative sample of adults in the United States. METHODS: Data for the current study was collected as part of Wave 8 Omnibus 2000 survey conducted by RAND ALP and consisted of 2080 participants. Responses were collected through the online RAND ALP survey in March 2021. RESULTS: Trust in the scientific community was the strongest predictor for already receiving at least one dose of the COVID-19 vaccine at the time of study. Asians had a significantly higher trust in the scientific community compared to all other groups. Results also showed a significant difference in level of trust of the government's response to the COVID-19 pandemic with Indian/Alaskan Natives reporting lower trust compared to Whites, Blacks and Asians. Asians also had a significantly higher level of trust when compared to those who identified as racial Other. Those who identify as American Indian/Alaskan Natives had the lowest levels of institutional trust. Trust in the government's response was not indicative of vaccination within the sample. CONCLUSIONS: Strategies to increase trust of the scientific community can be employed to address vaccine hesitancy through community-based initiatives and building of partnerships between the scientific community and local community stakeholders.
Subject(s)
COVID-19 , Vaccines , Adult , COVID-19 Vaccines , Humans , Pandemics , Race Factors , SARS-CoV-2 , Trust , United StatesABSTRACT
In this second golden age of data design, digital affordances enable the news media to share occasionally misleading charts about COVID-19. Examining data visualizations about COVID-19 highlights three ways that charts can mislead viewers: (a) by displaying inadequate data, (b) by manipulating scales and visual distance, and (c) by omitting contextual labels needed to fully understand a chart’s message. This article provides takeaways for technical communicators about including and displaying adequate data, representing numbers consistently, and humanizing COVID-19’s effects.
ABSTRACT
In this second golden age of data design, digital affordances enable the news media to share occasionally misleading charts about COVID-19. Examining data visualizations about COVID-19 highlights three ways that charts can mislead viewers: (a) by displaying inadequate data, (b) by manipulating scales and visual distance, and (c) by omitting contextual labels needed to fully understand a chart?s message. This article provides takeaways for technical communicators about including and displaying adequate data, representing numbers consistently, and humanizing COVID-19?s effects.