Evidence map›Paper›PMID 36592485›Full record

ArticleHealth & place2023

A cross-national study of multilevel determinants on public fully vaccination against COVID-19.

Feng Hao

Abstract read
In one paragraph

Article in Health & place, 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. 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

1 author.

Feng HaoDepartment of Sociology, University of South Florida, 8350 N. Tamiami Trail, Sarasota, FL, USA. Electronic address: fenghao@usf.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pandemic caused by SARS-CoV-2 (COVID-19) has impacted the world for close to three years and led to substantial costs to public well-being. To mitigate the pandemic's damage, the most effective approach lies in the vaccine. This study aims to investigate multilevel predictors of the public decision to become fully vaccinated against COVID-19. Data from a cross-national survey on representative samples are merged with country-level indicators. Multilevel logistic regression models are estimated on populations from 15 countries. Findings show that people who agree the government handles the pandemic well, believe the health officials would provide an effective vaccine, perceive the virus's danger, and are older are more likely to get fully vaccinated than their counterparts. Meanwhile, the national case rate and vaccination rate also affect one's decision to become fully vaccinated. Furthermore, there are significant cross-level interactions as people are more inclined to become fully vaccinated if they agree with the government's performance, perceive the virus's danger, and also reside in countries with higher case and vaccination rates. This study shows cross-national evidence regarding multilevel determinants of public vaccine uptake. Knowing the profiles among populations who have become fully vaccinated or not helps public health experts leverage factors and maximize vaccination.

Indexed as

COVID-19GovernmentHealth StatusHumansSARS-CoV-2VaccinationCOVID-19Cross-national designMultilevel estimationVaccine uptake

Identifiers

PMID36592485
PMCPMC9790879

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.