Evidence map›Paper›PMID 40378171›Full record

ArticlePloS one2025

Removing barriers to COVID-19 vaccine intention in a university population: Results of a serial mediation study through the dimensions of the Health Belief Model.

Marine Paridans, Nadia Dardenne, Nicolas Gillain, Eddy Husson, Christelle Meuris, Gilles Darcis, Michel Moutschen, Claude Saegerman, Laurent Gillet, Fabrice Bureau and 3 more

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Marine ParidansResearch unit Public Health: from Biostatistics to Health Promotion, University of Liège, Liège, Belgium.ORCID https://orcid.org/0000-0002-8084-9359
Nadia DardenneResearch unit Public Health: from Biostatistics to Health Promotion, University of Liège, Liège, Belgium.
Nicolas GillainResearch unit Public Health: from Biostatistics to Health Promotion, University of Liège, Liège, Belgium.ORCID https://orcid.org/0000-0001-9487-5139
Eddy HussonResearch unit Public Health: from Biostatistics to Health Promotion, University of Liège, Liège, Belgium.
Christelle MeurisInfectious Diseases Department, University Hospital of Liège, Liège, Belgium.
Gilles DarcisInfectious Diseases Department, University Hospital of Liège, Liège, Belgium.ORCID https://orcid.org/0000-0001-8192-1351
Michel MoutschenInfectious Diseases Department, University Hospital of Liège, Liège, Belgium.
Claude SaegermanFundamental and Applied Research for Animal and Health (FARAH) Centre, Liège University, Liège, Belgium.ORCID https://orcid.org/0000-0001-9087-7436
Laurent GilletLaboratory of Immunology-Vaccinology, FARAH, Liège University, Liège, Belgium.
Fabrice BureauLaboratory of Cellular and Molecular Immunology, GIGA Institute, Liège University, Liège, Belgium.
Anne-Françoise DonneauResearch unit Public Health: from Biostatistics to Health Promotion, University of Liège, Liège, Belgium.
Michèle GuillaumeResearch unit Public Health: from Biostatistics to Health Promotion, University of Liège, Liège, Belgium.
Benoit PétréResearch unit Public Health: from Biostatistics to Health Promotion, University of Liège, Liège, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile many studies have used the Health Belief Model (HBM) to understand vaccine intention, none claim to have used serial mediation to understand the relationship between HBM dimensions and COVID-19 vaccine intention. This study developed a serial mediation model to assess the direct and indirect effects of the latent HBM dimensions on COVID-19 primary vaccine intention.

methodsA cross-sectional study: from 01 April to 10 June 2021, a self-administered online questionnaire on vaccine intention against COVID-19 was distributed to staff and students at the University of Liège (Belgium). Direct and indirect effects of the HBM dimensions (perceived susceptibility, severity, benefits, barriers, self-efficacy and cues to action) on vaccine intention (score 0-100) were assessed with serial mediation models. Actually, each permutation of the latent HBM dimensions, i.e., each causal chain, was assessed using partial least squares path modelling (PLS-PM) according to the order of the HBM dimensions in that particular chain.

resultsThe sample was made up of 1256 participants. The final model revealed that the causal chain with the lowest Bayesian Information Criterion (BIC) value was barriers (Effect estimation (CI95%): -0.09 (-0.15 - -0.03)) ↘ severity (-0.13 (-0.20 - -0.07)) ↘ low self-efficacy (0.20 (0.15-0.25)) ↘ low susceptibility (-0.55 (-0.60 - -0.51)) ↘ vaccine intention (outcome). This revealed a significant indirect and direct effect (-0.20 (-0.25 - -0.15)) between barriers and vaccine intention.

conclusionsThe results demonstrated that perceived barriers are a key determinant in COVID-19 primary vaccine intention. Public health practitioners need to prioritise messaging that addresses the barriers reducing vaccine intention to enable individuals to make an informed choice. These messages could form part of a mass communication campaign aimed at hesitant individuals, with evidence-based information about vaccine safety a priority in order to establish a climate of trust.

Indexed as

COVID-19COVID-19 VaccinesHealth Belief ModelVaccinationAdolescentAdultBelgiumCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansIntentionMaleMiddle AgedSARS-CoV-2Self EfficacyCOVID-19 Vaccines

Identifiers

PMID40378171
PMCPMC12083829

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.