Bălăeț, Maria;
Kurtin, Danielle L;
Gruia, Dragos C;
Lerede, Annalaura;
Custovic, Darije;
Trender, William;
Jolly, Amy E;
... Hampshire, Adam; + view all
(2023)
Mapping the sociodemographic distribution and self-reported justifications for non-compliance with COVID-19 guidelines in the United Kingdom.
Frontiers in Psychology
, 14
, Article 1183789. 10.3389/fpsyg.2023.1183789.
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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.
| Type: | Article |
|---|---|
| Title: | Mapping the sociodemographic distribution and self-reported justifications for non-compliance with COVID-19 guidelines in the United Kingdom |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fpsyg.2023.1183789 |
| Publisher version: | https://doi.org/10.3389/fpsyg.2023.1183789 |
| Language: | English |
| Additional information: | © 2023 Bălăeț, Kurtin, Gruia, Lerede, Custovic, Trender, Jolly, Hellyer and Hampshire. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | COVID-19, compliance, topic modelling, natural language processing, behaviour |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10174777 |
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