Back

A Scoping Review of Factors used to Explain Disparities in COVID-19 Vaccination Intentions and Uptake among People of Color: United States, December 1, 2020-April 30, 2021

Wilson, R. F.; Kota, K. K.; Sheats, K.; Luna-Pinto, C.; Owens, C.; Harrison, D.; Razi, S.

2023-01-13 public and global health
10.1101/2023.01.12.23284499 medRxiv
Show abstract

BackgroundVaccine access, coupled with the belief that vaccines are important, beneficial, and safe, plays a pivotal role in achieving high levels of vaccination to reduce the spread and severity of COVID-19 in the United States (U.S.) and globally. Many factors can influence vaccine intentions and uptake. MethodsWe conducted a scoping review of factors (e.g., access-related factors, racism) known to influence vaccine intentions and uptake, using publications from various databases and websites published December 1, 2020-April 30, 2021. Descriptive statistics were used to present results. ResultsOverall, 1094 publications were identified through the database search, of which 133 were included in this review. Among the publications included, over 60% included mistrust in vaccines and vaccine-safety concerns, 43% included racism/discrimination, 35% included lack of vaccine access (35%), and 8% had no contextual factors when reporting on vaccine intentions and disparities in vaccine uptake. ConclusionsFindings revealed during a critical period when there was a well-defined goal for adult COVID-19 vaccination in the U.S., some publications included several contextual factors while others provided limited or no contextual factors when reporting on disparities in vaccine intentions and uptake. Failing to contextualize inequities and other factors that influence vaccine intentions and uptake might be perceived as placing responsibility for vaccination status on the individual, consequently, leaving social and structural inequities that impact vaccination rates and vaccine confidence, among people of color, intact.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.