Evidence map›Paper›PMID 35905384›Full record

ArticleHuman vaccines & immunotherapeutics2022

Understanding the impact process of vaccine adoption for COVID-19.

Wenlong Zhu, Hao Zou, Ying Song, Lili Ren, Yingjie Xu

Abstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Wenlong ZhuBusiness School, Qingdao University of Technology, Qingdao, China.
Hao ZouDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Ying SongBusiness School, Qingdao University of Technology, Qingdao, China.
Lili RenBusiness School, Qingdao University of Technology, Qingdao, China.
Yingjie XuCentral Laboratory, Qingdao Stomatological Hospital, Qingdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccination for the novel coronavirus disease 2019 (COVID-19) provides an effective approach for the general improvement of social safety and individual health. To date, few studies have analyzed the adoption of COVID-19 vaccines from an entire impact process perspective. Using the health belief model (HBM) and the valence theory, this research evaluates the impact process of vaccine adoption for COVID-19. The respondents in this study were individuals who have been vaccinated in China. The effective sample included 595 individuals. Four valuable and novel findings are identified through this research. First, neither perceived susceptibility nor perceived severity has a statistically significant impact on the benefits from vaccination, threats from vaccination and self-efficacy. Second, benefits from vaccination produce a significant positive effect on self-efficacy and vaccine adoption. Third, threats from vaccination produce a significant negative effect on self-efficacy and vaccine adoption. Fourth, both self-efficacy and cues to adoption produce a significantly positive impact on vaccine adoption. Our theoretical model, which is the main contribution of this research, indicates that individual vaccine adoption is simply a process that leads from behavioral cognition to behavioral intention, rather than from psychological perception to behavioral cognition and then from behavioral cognition to behavioral intention.

Indexed as

COVID-19VaccinesCOVID-19 VaccinesHumansIntentionVaccinationCOVID-19 VaccinesVaccineshealth belief model (HBM)Novel coronavirus disease 2019 (COVID-19)self-efficacyvaccine adoptionvalence theory

Identifiers

PMID35905384
PMCPMC9746427

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.