Gibson-Miller, J;
Zavlis, O;
Hartman, TK;
Bennett, KM;
Butter, S;
Levita, L;
Martinez, AP;
... Bentall, RP; + view all
(2022)
A network approach to understanding social distancing behaviour during the first UK lockdown of the COVID-19 pandemic.
Psychology and Health
10.1080/08870446.2022.2057497.
(In press).
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Abstract
Objective: Given the highly infectious nature of COVID-19, social distancing practices are key in stemming the spread of the virus. We aimed to assess the complex interplay among psychological factors, socio-demographic characteristics and social distancing behaviours within the framework of the widely used Capability, Opportunity, Motivation-Behaviour (COM-B) model. Design: The present research employed network psychometrics on data collected during the first UK lockdown in April 2020 as part of the COVID-19 Psychological Research Consortium (C19PRC) Study. Using a network approach, we examined the predictions of psychological and demographic variables onto social distancing practices at two levels of analysis: macro and micro. Results: Our findings revealed several factors that influenced social distancing behaviour during the first UK lockdown. The COM-B model was successful in predicting particular aspects of social-distancing via the influence of psychological capability and motivation at the macro-and micro-levels, respectively. Notably, demographic variables, such as education, income, and age, were directly and uniquely predictive of certain social distancing behaviours. Conclusion: Our findings reveal psychological factors that are key predictors of social distancing behaviour and also illustrate how demographic variables directly influence such behaviour. Our research has implications for the design of empirically-driven interventions to promote adherence to social distancing practices in this and future pandemics.
| Type: | Article |
|---|---|
| Title: | A network approach to understanding social distancing behaviour during the first UK lockdown of the COVID-19 pandemic |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/08870446.2022.2057497 |
| Publisher version: | https://doi.org/10.1080/08870446.2022.2057497 |
| Language: | English |
| Additional information: | © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | COVID-19; social distancing; behavioural science; intervention design; network psychometrics; complexity; COM-B model |
| UCL classification: | 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 > Imaging Neuroscience UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10147394 |
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