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Partisan self-identification predicts attitudes of South Dakota nurses toward COVID-19 vaccine mandate for healthcare workers.
Viskupic, Filip; Wiltse, David L.
Afiliação
  • Viskupic F; School of American and Global Studies, South Dakota State University, United States of America.
  • Wiltse DL; School of American and Global Studies, South Dakota State University, United States of America.
Health Policy Technol ; 12(3): 100777, 2023 Sep.
Article em En | MEDLINE | ID: mdl-37389329
ABSTRACT

Objectives:

Nurses are at greater risk of infection from COVID-19. However, mistrust of the vaccine exists even among this group. In the United States, the government implemented a vaccine mandate for health care workers to increase vaccination rates. This study investigated the drivers of nurses' attitudes toward the mandate.

Methods:

We fielded a survey to study the attitudes of nurses toward COVID-19 vaccine mandate for HCWs. We contacted nurses in South Dakota, United States, based on the information from the South Dakota Board of Nursing. The survey was open in June and July 2022. We conducted a multivariate regression analysis to identify the factors that predict attitudes toward this regulation.

Results:

We received 1,084 responses. Results of regression analysis showed statistically significant relationships between partisan self-identification, evangelical identity, gender, and COVID-19 vaccination status and support for COVID-19 vaccine mandate for healthcare workers. Age, time with patients, positive COIVD-19 test in the last year, education, and nurse classification variables were not statistically significant.

Conclusions:

The same factors that drive people's attitudes toward COVID-19 mitigation policies also explain nurses' attitudes toward a vaccine mandate for healthcare workers. The politicization of the COVID-19 pandemic is present also among nurses. Health care officials should be mindful of the influence of these biases as they evaluate the vaccine mandate and develop new regulations.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article