Evidence map›Paper›PMID 36127162›Full record

ArticleHealth policy (Amsterdam, Netherlands)2022

Lockdown support, trust and COVID-19 conspiracy beliefs: Insights from the second national lockdown in France.

Pauline Kergall, Marlène Guillon

Abstract read
In one paragraph

Article in Health policy (Amsterdam, Netherlands), 2022. 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

2 authors.

Pauline KergallUniversité de Montpellier, Montpellier Recherche en Economie, Avenue Raymond Dugrand 2, Montpellier 34960 Cedex, France. Electronic address: pauline.kergall@umontpellier.fr.
Marlène GuillonUniversité de Montpellier, Montpellier Recherche en Economie, Avenue Raymond Dugrand 2, Montpellier 34960 Cedex, France. Electronic address: marlene.guillon@umontpellier.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the COVID-19 pandemic, restrictive sanitary measures such as lockdowns have been implemented all around the world. Based on a representative sample of the population collected through an online cross-sectional survey, the goal of the study was to investigate the factors associated with lockdown agreement in France during the second general lockdown of fall 2020. More specifically, we aimed to investigate how trust in the government and COVID-19 conspiracy beliefs influenced lockdown agreement. Trust in the authorities and low adherence to conspiracy beliefs appeared as strong predictors of lockdown acceptance among our sample. Using a mediation analysis, we highlighted a significant indirect effect of trust in the authorities on lockdown agreement through the adherence to conspiracy beliefs: low level of trust translated into higher odds to believe in COVID-19 misinformation which in turn decreased lockdown support. The double effect of trust on lockdown agreement, both directly and indirectly, underlines the importance of careful communication from the government around decisions related to COVID-19 mitigation measures in order not to deteriorate even more the low level of trust in the health action of the government. The fight against false information also appears of the utmost importance to increase the population adherence to public authorities' recommendations.

Indexed as

COVID-19Communicable Disease ControlCross-Sectional StudiesHumansPandemicsTrustConspiracy BeliefsCOVID-19FranceLockdownMediation analysisTrust

Identifiers

PMID36127162
PMCPMC9472707

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.