Evidence map›Paper›PMID 41884998›Full record

ArticleHuman vaccines & immunotherapeutics2026

Covid-19 and influenza vaccine hesitancy insights from France, Italy and Hungary: A mixed-methods study.

Yongjin Choi, Emilie Karafillakis, Anna Perris, Leesa Lin, Alexandre de Figueiredo, Pauline Paterson

Abstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2026. 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

6 authors.

Yongjin ChoiDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, UK.ORCID 0000-0002-4085-062X
Emilie KarafillakisDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, UK.
Anna PerrisDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, UK.
Leesa LinDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, UK.
Alexandre de FigueiredoDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, UK.
Pauline PatersonDepartment of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the Covid-19 pandemic entered its endemic phase, France, Italy, and Hungary, as well as many other European countries, have observed a rise in vaccine hesitancy. This study employs a mixed-methods approach to understand the reasons behind this trend by linking statistical observations with in-depth view of vaccine hesitancy. A total of 3,021 adults were surveyed from France, Italy, and Hungary via nationally representative surveys, examining views toward these vaccines. We employed both pooled and un-pooled linear regression analyses. In each country, 20 semi-structured interviews were conducted. Our pooled survey analysis reveals that hesitancy toward Covid-19 and influenza vaccines was higher among respondents reporting mistrust in healthcare providers [95% CI = -0.26,-0.17;

Indexed as

COVID-19COVID-19 VaccinesInfluenza, HumanInfluenza VaccinesVaccination HesitancyAdolescentAdultAgedFemaleFranceHealth Knowledge, Attitudes, PracticeHumansHungaryItalyMaleMiddle AgedCOVID-19 VaccinesInfluenza VaccinesCovid-19in-depth interviewinfluenzamixed-methodssurveyVaccine hesitancy

Identifiers

PMID41884998
PMCPMC13023001

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.