Evidence map›Paper›PMID 37389329›Full record

ArticleHealth policy and technology2023

Partisan self-identification predicts attitudes of South Dakota nurses toward COVID-19 vaccine mandate for healthcare workers.

Filip Viskupič, David L Wiltse

Abstract read
In one paragraph

Article in Health policy and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Barriers to Long COVID Care in the U.S.: An Application of Levesque et al.'s Access Framework.Health care analysis : HCA : journal of health philosophy and policy · 2026
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Filip ViskupičSchool of American and Global Studies, South Dakota State University, United States of America.
David L WiltseSchool of American and Global Studies, South Dakota State University, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

attitudesCOVID-19NursesRegulationSurveyVaccine mandate

Identifiers

PMID37389329
PMCPMC10290765

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.