Evidence map›Paper›PMID 37539003›Full record

ArticleFrontiers in psychology2023

Mapping the sociodemographic distribution and self-reported justifications for non-compliance with COVID-19 guidelines in the United Kingdom.

Maria Bălăeț, Danielle L Kurtin, Dragos C Gruia, Annalaura Lerede, Darije Custovic, William Trender, Amy E Jolly, Peter J Hellyer, Adam Hampshire

Abstract read
In one paragraph

Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Maria BălăețDepartment of Brain Sciences, Imperial College London, London, United Kingdom.
Danielle L KurtinNeuromodulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
Dragos C GruiaDepartment of Brain Sciences, Imperial College London, London, United Kingdom.
Annalaura LeredeDepartment of Brain Sciences, Imperial College London, London, United Kingdom.
Darije CustovicDepartment of Brain Sciences, Imperial College London, London, United Kingdom.
William TrenderDepartment of Brain Sciences, Imperial College London, London, United Kingdom.
Amy E JollyNMR Unit, Queen Square Multiple Sclerosis Centre, UCL, Queen Square Institute of Neurology, Department of Neuroinflammation, Faculty of Brain Sciences, University College London, London, United Kingdom.
Peter J HellyerDepartment of Brain Sciences, Imperial College London, London, United Kingdom.
Adam HampshireDepartment of Brain Sciences, Imperial College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Which population factors have predisposed people to disregard government safety guidelines during the COVID-19 pandemic and what justifications do they give for this non-compliance? To address these questions, we analyse fixed-choice and free-text responses to survey questions about compliance and government handling of the pandemic, collected from tens of thousands of members of the UK public at three 6-monthly timepoints. We report that sceptical opinions about the government and mainstream-media narrative, especially as pertaining to justification for guidelines, significantly predict non-compliance. However, free text topic modelling shows that such opinions are diverse, spanning from scepticism about government competence and self-interest to full-blown conspiracy theories, and covary in prevalence with sociodemographic variables. These results indicate that attempts to counter non-compliance through argument should account for this diversity in peoples' underlying opinions, and inform conversations aimed at bridging the gap between the general public and bodies of authority accordingly.

Indexed as

behaviourcomplianceCOVID-19natural language processingtopic modelling

Identifiers

PMID37539003
PMCPMC10395087

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.