Dataset Information

Information From Same-Race/Ethnicity Experts Online Does Not Increase Vaccine Interest or Intention to Vaccinate.

ABSTRACT: Policy Points Mass vaccination is essential for bringing the COVID-19 pandemic to a close, yet substantial disparities remain between whites and racial and ethnic minorities within the United States. Online messaging campaigns featuring expert endorsements are a low-cost way to increase vaccine awareness among minoritized populations, yet the efficacy of same-race/ethnicity expert messaging in increasing uptake remains unknown. Our preregistered analysis of an online vaccine endorsement campaign, which randomly varied the racial/ethnic identity of the expert, revealed no evidence that information from same race/ethnicity experts affected vaccine interest or the intention to vaccinate. Our results do not rule out the possibility that other low-cost endorsement campaigns may be more effective in increasing vaccine uptake, but do suggest that public health campaigns might profitably focus on issues of access and convenience when targeting minoritized populations in the United States.

Context

The COVID-19 pandemic in the United States has been unequally experienced across racial and ethnic groups. Mass vaccination is the most effective way to bring the pandemic to an end and to manage its public health consequences. But the racialization of public health delivery in the United States has produced a sizable racial/ethnic gap in vaccination rates. Closing this gap in vaccine uptake is therefore essential to ending the pandemic.

Methods

We conducted a preregistered, well-powered (N = 2,117) between-subjects survey experiment, fielded March 24 to April 5, 2021, in which participants from YouGov's online panel-including oversamples of Black (n = 471), Hispanic/Latino/a (n = 430), and Asian American (n = 319) participants-were randomly assigned to see COVID-19 vaccine information endorsed by same- or different-race/ethnicity experts or to a control condition. We then measured respondents' vaccination intentions, intention to encourage others to get vaccinated, and interest in learning more information and sharing information with others.

Findings

Same-race/ethnicity expert endorsements had no measurable effect on nonwhite or white respondents' willingness to get the COVID-19 vaccine, to encourage others to get the vaccine, or to learn more or share information with others.

Conclusions

Our study provides empirical evidence suggesting online endorsements from same-race/ethnicity experts do not increase vaccine interest, advocacy, or uptake, though same-race/ethnicity endorsements may be effective in other venues or mediums.

SUBMITTER: Gadarian SK

PROVIDER: S-EPMC9111148 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Information From Same-Race/Ethnicity Experts Online Does Not Increase Vaccine Interest or Intention to Vaccinate.

Gadarian Shana Kushner SK Goodman Sara Wallace SW Michener Jamila J Nyhan Brendan B Pepinsky Thomas B TB

The Milbank quarterly 20220322 2

Policy Points Mass vaccination is essential for bringing the COVID-19 pandemic to a close, yet substantial disparities remain between whites and racial and ethnic minorities within the United States. Online messaging campaigns featuring expert endorsements are a low-cost way to increase vaccine awareness among minoritized populations, yet the efficacy of same-race/ethnicity expert messaging in increasing uptake remains unknown. Our preregistered analysis of an online vaccine endorsement campaign ...[more]

PMID: 35315950

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Project description:Increasing biomedical workforce diversity remains a persistent challenge. Recent reports have shown that biomedical sciences (BMS) graduate students become less interested in faculty careers as training progresses; however, it is unclear whether or how the career preferences of women and underrepresented minority (URM) scientists change in manners distinct from their better-represented peers. We report results from a survey of 1500 recent American BMS Ph.D. graduates (including 276 URMs) that examined career preferences over the course of their graduate training experiences. On average, scientists from all social backgrounds showed significantly decreased interest in faculty careers at research universities, and significantly increased interest in non-research careers at Ph.D. completion relative to entry. However, group differences emerged in overall levels of interest (at Ph.D. entry and completion), and the magnitude of change in interest in these careers. Multiple logistic regression showed that when controlling for career pathway interest at Ph.D. entry, first-author publication rate, faculty support, research self-efficacy, and graduate training experiences, differences in career pathway interest between social identity groups persisted. All groups were less likely than men from well-represented (WR) racial/ethnic backgrounds to report high interest in faculty careers at research-intensive universities (URM men: OR 0.60, 95% CI: 0.36-0.98, p = 0.04; WR women: OR: 0.64, 95% CI: 0.47-0.89, p = 0.008; URM women: OR: 0.46, 95% CI: 0.30-0.71, p<0.001), and URM women were more likely than all other groups to report high interest in non-research careers (OR: 1.93, 95% CI: 1.28-2.90, p = 0.002). The persistence of disparities in the career interests of Ph.D. recipients suggests that a supply-side (or "pipeline") framing of biomedical workforce diversity challenges may limit the effectiveness of efforts to attract and retain the best and most diverse workforce. We propose incorporation of an ecological perspective of career development when considering strategies to enhance the biomedical workforce and professoriate through diversity.

Dataset Information

Information From Same-Race/Ethnicity Experts Online Does Not Increase Vaccine Interest or Intention to Vaccinate.

Context

Methods

Findings

Conclusions

Publications

Information From Same-Race/Ethnicity Experts Online Does Not Increase Vaccine Interest or Intention to Vaccinate.

Similar Datasets

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