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Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

July 2022
PNAS Nexus

DOI:10.1093/pnasnexus/pgac093

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CC BY 4.0

Authors:

Tomislav Pavlović

Institute of Social Sciences Ivo Pilar

Flávio Azevedo

University of Cambridge

Koustav de

University of Michigan

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At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic. Keywords: COVID-19, social distancing, hygiene, policy support, psychology, machine learning, public health measures

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ORIGINAL UNEDITED MANUSCRIPT

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

Tomislav Pavlović1

Flavio Azevedo2

Koustav De3

Julián C. Riaño-Moreno4

Marina Maglić1

Theofilos Gkinopoulos5

Patricio Andreas Donnelly-Kehoe6

César Payán-Gómez7

Guanxiong Huang8

Jaroslaw Kantorowicz9

Michèle D. Birtel10

Philipp Schönegger11

Valerio Capraro12

Hernando Santamaría-García13

Meltem Yucel14

Agustin Ibanez15,16,17,18

Steve Rathje19

Erik Wetter20

Dragan Stanojević21

Jan-Willem van Prooijen22

Eugenia Hesse23

Christian T. Elbaek24

Renata Franc1

Zoran Pavlović25

Panagiotis Mitkidis24

Aleksandra Cichocka26

Michele Gelfand27

Mark Alfano28

Robert M. Ross28

Hallgeir Sjåstad29

John B. Nezlek30

Aleksandra Cislak31

Patricia Lockwood32,33

Koen Abts34

Elena Agadullina35

David M. Amodio36

Matthew A. J. Apps32

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John Jamir Benzon Aruta37

Sahba Besharati38

Alexander Bor39

Becky Choma40

William Cunningham41

Waqas Ejaz42

Harry Farmer43

Andrej Findor44

Biljana Gjoneska45

Estrella Gualda46

Toan L. D. Huynh47

Mostak Ahamed Imran48

Jacob Israelashvili49

Elena Kantorowicz-Reznichenko50

André Krouwel51

Yordan Kutiyski52

Michael Laakasuo53

Claus Lamm54

Jonathan Levy55,56

Caroline Leygue57

Ming-Jen Lin58

Mohammad Sabbir Mansoor59

Antoine Marie39

Lewend Mayiwar60

Honorata Mazepus61

Cillian McHugh62

Andreas Olsson63

Tobias Otterbring64

Dominic Packer65

Jussi Palomäki66

Anat Perry49

Michael Bang Petersen39

Arathy Puthillam67

Tobias Rothmund68

Petra C. Schmid69

David Stadelmann70

Augustin Stoica71

Drozdstoy Stoyanov72

Kristina Stoyanova73

Shruti Tewari74

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Bojan Todosijević75

Benno Torgler76

Manos Tsakiris77,78

Hans H. Tung79

Radu Gabriel Umbreș80

Edmunds Vanags81

Madalina Vlasceanu82

Andrew J. Vonasch83

Yucheng Zhang84

Mohcine Abad85

Eli Adler49

Hamza Alaoui Mdarhri85

Benedict Antazo86

F. Ceren Ay87

Mouhamadou El Hady Ba88

Sergio Barbosa89

Brock Bastian90

Anton Berg66

Michał Białek91

Ennio Bilancini92

Natalia Bogatyreva93

Leonardo Boncinelli94

Jonathan E. Booth95

Sylvie Borau96

Ondrej Buchel97,98

Chrissie Ferreira de Carvalho99

Tatiana Celadin100

Chiara Cerami101

Hom Nath Chalise102

Xiaojun Cheng103

Luca Cian104

Kate Cockcroft38

Jane Conway105

Mateo A. Córdoba-Delgado106

Chiara Crespi107

Marie Crouzevialle69

Jo Cutler108,109

Marzena Cypryańska30

Justyna Dabrowska110

Victoria H. Davis111

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John Paul Minda112

Pamala N. Dayley113

Sylvain Delouvée114

Ognjan Denkovski115

Guillaume Dezecache116

Nathan A. Dhaliwal117

Alelie Diato118

Roberto Di Paolo92

Uwe Dulleck119

Jānis Ekmanis120

Tom W. Etienne52

Hapsa Hossain Farhana121

Fahima Farkhari122,123

Kristijan Fidanovski124

Terry Flew125

Shona Fraser126

Raymond Boadi Frempong70

Jonathan Fugelsang127

Jessica Gale83

E. Begoña García-Navarro128

Prasad Garladinne74

Kurt Gray129

Siobhán M. Griffin62

Bjarki Gronfeldt26

June Gruber130

Eran Halperin49

Volo Herzon66

Matej Hruška44

Matthias F. C. Hudecek131

Ozan Isler119

Simon Jangard63

Frederik Jørgensen39

Oleksandra Keudel132

Lina Koppel133

Mika Koverola66

Anton Kunnari134

Josh Leota135

Eva Lermer136,137

Chunyun Li95

Chiara Longoni138

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Darragh McCashin139

Igor Mikloušić1

Juliana Molina-Paredes140

César Monroy-Fonseca141

Elena Morales-Marente142

David Moreau143

Rafał Muda144

Annalisa Myer145

Kyle Nash146

Jonas P. Nitschke54

Matthew S. Nurse147

Victoria Oldemburgo de Mello41

M. Soledad Palacios-Galvez148

Jussi Palomäki66

Yafeng Pan149

Zsófia Papp150

Philip Pärnamets63

Mariola Paruzel-Czachura151,152

Silva Perander153

Michael Pitman38

Ali Raza154

Gabriel Gaudencio Rêgo155

Claire Robertson156

Iván Rodríguez-Pascual148

Teemu Saikkonen157

Octavio Salvador-Ginez158

Waldir M. Sampaio155

Gaia Chiara Santi159

David Schultner160

Enid Schutte38

Andy Scott146

Ahmed Skali161

Anna Stefaniak162

Anni Sternisko156

Brent Strickland164

Brent Strickland163

Jeffrey P. Thomas95

Gustav Tinghög133

Iris J. Traast165

Raffaele Tucciarelli166

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Michael Tyrala167

Nick D. Ungson168

Mete Sefa Uysal169

Dirk Van Rooy170

Daniel Västfjäll171

Joana B. Vieira63,172

Christian von Sikorski173

Alexander C. Walker127

Jennifer Watermeyer174

Robin Willardt69

Michael J. A. Wohl162

Adrian Dominik Wójcik175

Kaidi Wu176

Yuki Yamada177

Onurcan Yilmaz178

Kumar Yogeeswaran83

Carolin-Theresa Ziemer68

Rolf A. Zwaan179

Paulo Sergio Boggio155

Ashley Whillans180

Paul A. M. Van Lange22

Rajib Prasad181

Michal Onderco182

Cathal O'Madagain85

Tarik Nesh-Nash183

Oscar Moreda Laguna52

Yordan Kutiyski52

Emily Kubin173

Mert Gümren184

Ali Fenwick185

Arhan S. Ertan186

Michael J. Bernstein187

Hanane Amara183

Jay Joseph Van Bavel156

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1Institute of Social Sciences Ivo Pilar, Zagreb, Croatia

2Department of Psychology, School of the Biological Sciences, Cambridge University, Cambridge,

United Kingdom

3Department of Finance and Quantitative Methods, Gatton College of Business & Economics,

University of Kentucky, Lexington, Kentucky, United States of America

4Faculty of Medicine, Cooperative University of Colombia, Villavicencio, Meta, Colombia

5Department of Philosophy and Social Studies, University of Crete, Rethymnon, Crete, Greece

6Department of Research and Development, Kozaca SA, Rosario, Santa Fe, Argentina

7Direccion Academica Sede la Paz, Universidad Nacional de Colombia - Sede de La Paz, Cesar,

Colombia

8Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong

9Department of Economics, Institute of Security and Global Affairs, Leiden University, The Hague,

Netherlands

10School of Human Sciences, University of Greenwich, London, United Kingdom

11Department of Philosophy, University of St Andrews, St Andrews, United Kingdom

12Department of Economics, Middlesex University London, London, United Kingdom

13Department of Psychiatry, Pontifical Xavierian University, Bogotá, Colombia

14Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, United

States of America

15Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Peñalolén,

Chile

16Cognitive Neuroscience Center (CNC), University of San Andrés & CONICET, Buenos Aires,

Argentina, Buenos Aires, Argentina

17Global Brain Health Institute, University of California - San Francisco, San Francisco, California,

United States of America

18Global Brain Health Institute, Trinity College, Dublin, Ireland

19Department of Psychology, University of Cambridge, Cambridge, United Kingdom

20Department of Business Administration, Stockholm School of Economics, Stockholm, Sweden

21Department of Sociology, Faculty of Philosophy, University of Belgrade, Belgrade, Serbia

22Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam,

Netherlands

23Departamento de Matemática y Ciencias, Universidad de San Andres, Victoria, Buenos Aires,

Argentina

24Department of Management, Aarhus University, Aarhus, Denmark

25Department of Psychology, Faculty of Philosophy, University of Belgrade, Belgrade, Serbia

26School of Psychology, University of Kent, Canterbury, United Kingdom

27Stanford Graduate School of Business, Stanford University, Stanford, California, United States of

America

28Department of Philosophy, Macquarie University, Macquarie Park, New South Wales, Australia

29Department of Strategy and Management, Norwegian School of Economics, Bergen, Norway

30Institute of Psychology, Center for Climate Action and Social Transformations, SWPS, University of

Social Sciences and Humanities, Warsaw, Poland

31Institute of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland

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32Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham,

United Kingdom

33Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

34Centre for Sociological Research, Katholieke Universiteit Leuven, Leuven, Belgium

35Faculty of Psychology, Higher School of Economics University, Moscow, Russia

36Department of Psychology, University of Amsterdam, Amsterdam, Netherlands

37Department of Psychology, Sunway University, Petaling Jaya, Selangor, Malaysia

38Department of Psychology, University of the Witwatersrand, Johannesburg, Republic of South Africa

39Department of Political Science, Aarhus University, Aarhus, Denmark

40Department of Psychology, Toronto Metropolitan University, Toronto, Ontario, Canada

41Department of Psychology, University of Toronto, Toronto, Ontario, Canada

42Department of Mass Communication, National University of Science and Technology (NUST),

Islamabad, Islamabad Capital Territory, Pakistan

43Department of Psychology, University of Greenwich, London, United Kingdom

44Institute of European Studies and International Relations, Faculty of Social and Economic Sciences,

Comenius University, Bratislava, Slovakia

45Department of Psychology, Macedonian Academy of Sciences and Arts, Skopje, Republic of North

Macedonia

46Department of Sociology, Social Work and Public Health, University of Huelva, Huelva, Spain

47Department of Decision Analytics and Risk, University of Southampton, Southampton, United

Kingdom

48Department of Educational and Counselling Psychology, BRAC Institute of Educational and

Development, BRAC University, Dhaka, Bangladesh

49Department of Psychology, Hebrew University of Jerusalem, Jerusalem, Israel

50Rotterdam Institute of Law and Economics (RILE), Erasmus University Rotterdam, Rotterdam,

Netherlands

51Department of Communication Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

52Kieskompas (Election Compass), Amsterdam, Netherlands

53Department of Digital Humanities, University of Helsinki, Helsinki, Finland

54Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Wien, Austria

55Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland

56Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel

57School of Psychology, Universidad Nacional Autónoma de Mexico, Mexico City, Mexico

58Department of Economics, National Taiwan University, Taipei, Taiwan

59HEMS School, Kathmandu, Nepal

60Department of Leadership and Organizational Behaviour, BI Norwegian Business School, Oslo,

Norway

61Institute of Security and Global Affairs, Leiden University, The Hague, Netherlands

62Department of Psychology, University of Limerick, Limerick, Ireland

63Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

64Department of Management, University of Agder, Kristiansand, Norway

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65Department of Psychology, Lehigh University, Bethlehem, Pennsylvania, United States of America

66Department of Digital Humanities, University of Helsinki, Helsinki, Finland

67Department of Psychology, Monk Prayogshala, Powai, Mumbai, Maharashtra, India

68Department of Communication and Media Use, Friedrich Schiller University Jena, Jena, Germany

69Department of Management, Technology, and Economics, Swiss Federal Institute of Technology in

Zürich, Zürich, Switzerland

70Chair of Development Economics, University of Bayreuth, Bayreuth, Germany

71Department of Sociology, National School for Political and Administrative Studies (SNSPA),

Bucharest, Romania

72Department of Psychiatry and Medical Psychology, Medical University, Plovdiv, Bulgaria

73Division of Translational Neuroscience, Medical University, Plovdiv, Bulgaria

74Department of Humanities and Social Sciences, Indian Institute of Management, Indore, Madhya

Pradesh, India

75Department of Psychology, Institute of Social Sciences, Belgrade, Serbia

76School of Economics and Finance and Centre for Behavioural Economics, Society and Technology

(BEST), Queensland University of Technology, Brisbane City, Queensland, Australia

77Department of Psychology, Royal Holloway, University of London, Egham, United Kingdom

78Centre for the Politics of Feelings, School of Advanced Study, University of London, London, United

Kingdom

79Department of Political Science, National Taiwan University, Taipei, Taiwan

80Faculty of Political Science, National University of Political Studies and Public Administration,

Bucharest, Romania

81Psychology Department, University of Latvia, Riga, Latvia

82Department of Psychology, New York University, New York, New York, United States of America

83Department of Psychology, University of Canterbury, Christchurch, New Zealand

84School of Economics and Management, Hebei University of Technology, Tianjin, China

85School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco

86Department of Psychology, Jose Rizal University, Mandaluyong, Metro Manila, Philippines

87Department of Economics, Telenor Research, Fornebu, Norway

88Cheikh Anta Diop University, Dakar, Senegal

89School of Medicine and Health Sciences, Universidad del Rosario, Bogotá, Colombia

90Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria,

Australia

91Faculty of Historical and Pedagogical Sciences, University of Wroclaw, Wroclaw, Poland

92IMT School for Advanced Studies, Lucca, Italy

93Laboratory for Psychology of Social Inequality, Higher School of Economics University, Moscow,

Russia

94Department of Economics and Management, University of Florence, Florence, Italy

95Department of Management, London School of Economics and Political Science, London, United

Kingdom

96Department of Marketing, TBS Education, Toulouse, France

97The Institute for Sociology, Slovak Academy of Sciences, Bratislava, Slovakia

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98Social Policy Institute, Ministry of Labor, Family and Social Affairs of the Slovak Republic,

Bratislava, Slovakia

99Department of Psychology, Federal University of Santa Catarina, Florianopolis, Brazil

100Department of Economics, University of Bologna, Bologna, Italy

101IUSS Cognitive Neuroscience Center, University School for Advanced Studies, Pavia, Italy

102Central Department of Population Studies, Tribhuvan University, Kathmandu, Nepal

103School of Psychology, Shenzhen University, Shenzhen, China

104Department of Marketing, Darden School of Business, University of Virginia, Charlottesville,

Virginia, United States of America

105MRC Social, Genetic and Developmental Psychiatry Centre, Institute for Advanced Study in

Toulouse, Université Toulouse 1 Capitole, Toulouse Cedex 6, France

106Faculty of Medicine, Pontifical Xavierian University, Bogotá, Colombia

107U.O. Rho, Fondazione Luigi Clerici, Rho, Italy

108School of Psychology, University of Birmingham, Birmingham, United Kingdom

109School of Psychology, University of Oxford, Oxford, United Kingdom

110Department of Trade and Market Institutions, Cracow University of Economics, Kraków, Poland

111Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health,

University of Toronto, Toronto, Ontario, Canada

112Department of Psychology, Western University, London, Ontario, Canada

113Department of Psychology, University of California - Los Angeles, Los Angeles, California, United

States of America

114Department of Psychology, Université Rennes 2, Rennes, France

115Department of Communication Science, University of Amsterdam, Amsterdam, Netherlands

116Université Clermont Auvergne, Clermont-Ferrand, France

117UBC Sauder School of Business, University of British Columbia, Vancouver, British Columbia,

Canada

118Teacher Education Department, Cavite State University, General Trias, Cavite, Philippines

119School of Economics and Finance, Queensland University of Technology, Brisbane City,

Queensland, Australia

120Faculty of Education, Psychology and Art, University of Latvia, Riga, Latvia

121National Institute for the Intellectually Disabled and Autistic (NIIDA), Society for the Welfare of the

Intellectually Disabled (SWID Bangladesh), Dhaka, Bangladesh

122Department of Communication and Media Use, University of Münster, Münster, Germany

123Friedrich Schiller University Jena, Jena, Germany

124Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom

125Department of Media and Communications, University of Sydney, Sydney, New South Wales,

Australia

126Medical School, Department of Psychiatry, University of the Witwatersrand, Johannesburg,

Republic of South Africa

127Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada

128Department of Nursing, University of Huelva, Huelva, Spain

129Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina,

United States of America

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130Department of Psychology and Neuroscience, College of Arts and Sciences, University of Colorado

Boulder, Boulder, Colorado, United States of America

131Department of Experimental Psychology, University of Regensburg, Regensburg, Germany

132Kyiv School of Economics, Kyiv, Ukraine

133Department of Management and Engineering, Linköping University, Linköping, Sweden

134University of Helsinki, Helsinki, Finland

135Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia

136Department of Business and Media Psychology, Ansbach University of Applied Sciences, Ansbach,

Germany

137Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich,

Germany

138Department of Marketing, Boston University, Questrom School of Business, Boston,

Massachusetts, United States of America

139School of Psychology, Dublin City University, Dublin, Ireland

140Pontifical Xavierian University, Bogotá, Colombia

141Seele Neuroscience, Mexico City, Mexico

142Department of Social, Developmental and Educational Psychology, University of Huelva, Huelva,

Spain

143School of Psychology, University of Auckland, Auckland, New Zealand

144Faculty of Economics, Maria Curie Sklodowska University, Lublin, Poland

145Department of Psychology, City University of New York (CUNY) Graduate Center, New York, New

York, United States of America

146Department of Psychology, University of Alberta, Edmonton, Alberta, Canada

147Australian National Centre for the Public Awareness of Science, Australian National University,

Canberra ACT, Australia

148COIDESO-Research Center of Contemporary Thinking and Innovation for Social Development,

University of Huelva, Huelva, Spain

149Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China

150Department for Political Behavior, Centre for Social Sciences, Budapest, Hungary

151Institute of Psychology, University of Silesia in Katowice, Katowice, Poland

152Facultad de Psicología, Complutense University of Madrid, Madrid, Spain

153Department of Computer Science, University of Helsinki, Helsinki, Finland

154Department of Computer Science, Institute of Cognitive Science, University of Colorado Boulder,

Boulder, Colorado, United States of America

155Centro de Ciências Biológicas e da Saúde, Mackenzie Presbyterian University, São Paulo, Brazil

156Department of Psychology & Neural Science, New York University, New York, New York, United

States of America

157Department of Biology, Biodiversity Unit, University of Turku, Turku, Finland

158School of Psychology, Environmental Psychology Department, National Autonomous University of

Mexico, Mexico City, Mexico

159Department of Humanities and Life Sciences, University School for Advanced Studies, Pavia, Italy

160Department of Social Psychology, University of Amsterdam, Amsterdam, Netherlands

161Department of Global Economics & Management, University of Groningen, Groningen, Netherlands

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162Department of Psychology, Carleton University, Ottawa, Ontario, Canada

163Department of cognitive studies, Ecole normale supérieure, Institut Jean Nicod, Paris, France

164Africa Business School and The School of Collective Intelligence, UM6P, Rabat, Morocco

165Social Psychology Department, University of Amsterdam, Amsterdam, Netherlands

166Institute of Cognitive Neuroscience, University College London, London, United Kingdom

167Department of Asian and International Studies, City University of Hong Kong, Kowloon Tong, Hong

Kong

168Department of Psychology, Susquehanna University, Selinsgrove, Pennsylvania, United States of

America

169Department of Social Psychology, Friedrich Schiller University Jena, Jena, Germany

170Department of Design, University of Antwerp, Antwerp, Belgium

171Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden

172Department of Psychology, University of Exeter, Exeter, United Kingdom

173Department of Psychology, University of Koblenz-Landau, Landau, Germany

174Department of Speech Pathology and Audiology, University of the Witwatersrand, Johannesburg,

Republic of South Africa

175Institute of Psychology, Nicolaus Copernicus University, Torun, Poland

176Rady School of Management, University of California, San Diego, United States of America

177Faculty of Arts and Science, Kyushu University, Fukuoka, Japan

178Department of Psychology, Kadir Has University, Fatih/Istanbul, Turkey

179Department of Psychology, Erasmus University Rotterdam, Rotterdam, Netherlands

180Faculty of Negotiations, Organizations and Markets, Harvard Business School, Boston,

Massachusetts, United States of America

181Department of Economics, Vidyasagar College For Women, Kolkata, West Bengal, India

182Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam,

Netherlands

183Impact For Development, Casablanca, Morocco

184Department of Economics, Koc University, Sarıyer/Istanbul, Turkey

185Hult International Business School, Dubai, United Arab Emirates

186Department of International Trade, Bogazici University, Besiktas/Istanbul, Turkey

187Department of Psychological and Social Sciences, Penn State University Abington College,

Abington, Pennsylvania, United States of America

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Abstract

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize

the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the

characteristics determining attitudinal and behavioral responses to the pandemic is crucial to

improving future interventions. In this study, we applied machine learning on the multi-national data

collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N =

51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality

psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the

pandemic. The results point to several valuable insights. Internalized moral identity provided the most

consistent predictive contribution - individuals perceiving moral traits as central to their self-concept

reported higher adherence to preventive measures. Similar was found for morality as cooperation,

symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse

relationship was evident for the endorsement of conspiracy theories. However, we also found a non-

negligible variability in the explained variance and predictive contributions with respect to macro-level

factors such as the pandemic stage or cultural region. Overall, the results underscore the importance

of morality-related and contextual factors in understanding adherence to public health

recommendations during the pandemic.

Keywords: COVID-19, social distancing, hygiene, policy support, psychology, machine learning,

public health measures

Significance statement

Outcomes of this study suggest that morality-related factors, along with prosociality and individual

characteristics related to information processing and self-control, play an important role in determining

attitudinal and behavioral responses to the COVID-19 pandemic. However, a substantial variation in

the predictive contribution of included variables was observed. Therefore, the role of context (both in

terms of culture and stage of the pandemic) should not be underestimated. Nevertheless, this study

highlighted multiple factors relevant to the prevention of COVID-19 in different stages of the pandemic

and cultures, which makes it a good starting point for more complex and causal research designs.

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Introduction

The COVID-19 pandemic has caused significant loss of life, commodities, jobs, and disruption of

communities worldwide. As of March 2022, over 450 million infections and more than 6 million deaths

have been reported globally (WHO, 2022). As we write this paper, the daily number of new cases

worldwide exceeds one million. Given the lack of vaccination and treatment options, controlling the

spread of the SARS-CoV-2 virus in its early stages depended on preventive behaviors, such as

physical distancing (Thu et al., 2020) or hand and object disinfection (Alzyood et al., 2020, Meyers et

al., 2021). While governments across the globe rushed to implement the proposed measures, many

citizens resisted such change (Ryu et al., 2020, Van Zandwijk & Rasko, 2020). This is indicative of the

vital role of individual characteristics in the form of attitudes, abilities, traits, and perceptions in

compliance with preventive measures. Thus, decision-makers may benefit from insights from the

social and behavioral sciences that could explain who will adhere to or ignore advised measures (Van

Bavel et al., 2020).

Furthermore, nations vary in the strictness of preventive measures enacted by local governments and

the severity of the consequences of COVID-19: some countries report more than 100 deaths (e.g.,

Croatia, the UK), while others count less than one death (e.g., Bhutan, China) per 100,000 citizens

(Johns Hopkins Coronavirus Resource Center, n.d.). A recent cross-national analysis suggests that

many of these excess deaths in countries like the USA are the result of weak public health

infrastructure and a decentralized, inconsistent response to the pandemic (Bilinski & Emanuel, 2020).

This raises questions of how macro-level cultural variables might be associated with citizens’ health

attitudes and behaviors across nations.

The scientific community responded with numerous international research collaborations aimed at

explaining adherence to preventive measures from different perspectives. One group of researchers

(Travaglino & Moon, 2021) focused on cultural dimensions, self-awareness emotions, trust in

governmental actions, and political orientation as predictors of compliance in the USA, Italy, and

Korea. They found that horizontal collectivism was the only predictor of compliance significant in all

three countries. Similarly, other scholars (Biddlestone et al., 2020) identified collectivism's role in

promoting preventive behaviors. In terms of adherence to preventive measures, prosocial tendencies

emerged as a significant positive predictor, while perceiving others as violating preventive measures

was the most consistent negative predictor (Coroiu et al., 2020). Results from another study across

70 countries showed that trust in government, conscientiousness, and agreeableness predicted

engaging in preventive measures, with other variables having a negligible practical impact (Clark et

al., 2020). As research accumulates, interpreting and integrating findings from diverse research

streams with a variety of measures and samples presents another challenge for both scholars and

practitioners.

Due to their freedom of theoretical constraints, data-driven approaches might offer solutions to “grand

challenges” of existing theories, defined as complex problems with intertwined and evolving

underlying mechanisms (Eisenhardt et al., 2016) as they allow the effective use of highly dimensional

data (Igarashi et al., 2016). For instance, network analysis was used on data from the UK and the

Netherlands to explore the relationship between multiple constructs relevant for COVID-19 attitudes

and behaviors (Chambon et al., 2021). The perceived level of adherence to norms and efficacy of

preventive measures and support for these measures exhibited the strongest relationships with

COVID-19 preventive behaviors. On the other hand, applying random forests on more than 100

potentially relevant variables established different descriptive and injunctive norms and prosociality as

some of the most relevant predictors of behaviors (Van Lissa et al., 2022). Overall, the relevance of

prosociality and collectivism, both of which imply a willingness to make sacrifices for the benefit of the

community, has been emphasized throughout the literature (Biddlestone et al., 2020; Capraro et al.

2021; Coroiu et al., 2020; Travaglino & Moon, 2021; Van Bavel et al., 2022; Van Lissa et al., 2022).

However, this does not eliminate the role of other individual differences and capacities in adherence

to preventive measures (Clark et al., 2020; Van Lissa et al., 2022).

Our study expands on and contributes to the existing literature in three important ways. Firstly, we

brought together a diverse team of experts to select several key constructs from social, moral,

cognitive, and personality psychology that might be relevant to supporting public health

recommendations. Despite a proliferation of studies on predictors of attitudinal and behavioral

responses to the COVID-19 pandemic (see, for example, Zettler et al., 2021), research in this field is

still warranted. Hence, we sought to investigate attitudinal and behavioral responses in the first

pandemic wave, when uncertainty regarding the spread of the virus dominated societies. In

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conjunction with the existing findings, our study provides valuable evidence which can be utilized to

compare pandemic responses from different time points during the pandemic. Moreover, we sought to

statistically test the association between the three related but distinct outcomes – maintaining physical

hygiene, avoiding physical contact, and supporting governmental policies related to COVID-19. This

distinguishes our approach from prior research that employed a general factor of preventive behaviors

as it allowed us to gain insights both into attitudinal and behavioral responses to the measures aimed

at mitigating the spread of the virus. Secondly, we consider potential cultural differences in the

meaning of the studied constructs by establishing equivalence of factor scores through (partial) strong

invariance (see Putnick & Bornstein, 2016). Finally, to determine the efficacy of our independent

variables in explaining contact avoidance, hygiene maintenance, and COVID-19 policy support in

each country, we applied random forest-based regression algorithms appropriate for complex data

sets with possible non-linear and interactive relationships between variables (Breiman, 2001; Sage,

2018).

Overview

We focused on two specific research questions utilizing a large international sample of 51,404

participants from 69 countries from all continents except Antarctica. Firstly, we tested how precisely

(in terms of the explained variance) avoiding contact, maintaining hygiene, and policy support could

be predicted using a combination of variables from moral, social, personality, and cognitive

psychology, as well as socio-demographic variables. Secondly, we tested which of the included

variables provided a substantial contribution to the accuracy of our predictions. Descriptions of the

expected effects (of all study variables) based on theories or earlier studies are available as

Supplementary materials A1. Additionally, to evaluate the robustness of our findings, we conducted

additional analyses that took cultural differences and the pandemic stage during data collection into

account.

1 All supplementary files can be found online in the following OSF folder:

https://osf.io/cvkyr/?view_only=c88c0431224c4f878750875e599d2983

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Materials and Methods

International Collaboration on the Social & Moral Psychology of COVID-19 Project

The aim of the International Collaboration on the Social & Moral Psychology of COVID-19 (ICSMP

COVID-19) project is to examine and understand psychological factors related to the COVID-19

pandemic response. We launched the project in April 2020 via a social media call for national teams

that could collect samples in their own country. Over 230 scholars responded to the call. The main

questionnaire, created in English, was disseminated to each national team, responsible for translating

it to their local language (using the standard forward-backwards method). Each team collected the

data in their own country. The resulting datasets were then collated and analyzed altogether, and are

available online (Azevedo et al., 2022). The study received an umbrella ethics approval from the

University of Kent.

Participants

The analyzed sample consisted of 51,404 participants from 69 countries and territories, 25 of which

collected samples representative of their respective nations regarding age and gender (n = 22,064).

The remaining data were drawn from convenience samples. Following exclusion criteria set for the

purposes of this study (we excluded participants providing inaccurate response to the attention check,

participants who did not provide responses to more than one quarter of items, participants providing

the same response more than ten times in a row on the items of our predictors, participants who

chose “other” as their gender and participants completing the questionnaire unusually fast or

unusually slow, see Supplementary materials B and C), 7,615 participants were removed, resulting in

a sample of 43,789 (Mage = 43, SDage = 16; 52% females) participants for our analyses.

Measures

Unless otherwise indicated, participants responded on an 11-point scale with higher values indicating

higher levels of the measured concepts (after reversing the appropriate items). Prior to conducting

analyses that presumed grouping of participants, we achieved partial strong invariance for all of the

included multi-item scales. This was important to ensure that we measured the same constructs with

similar efficacy in each group (see Putnick & Bornstein, 2016). Detailed output on how the fit was

achieved can be found in Supplementary material C.

Individual-level variables

Criteria

Avoidance of physical contact during the coronavirus (COVID-19) pandemic was measured via five

items. Adequate fit (CFI = 0.979, RMSEA (95% CI upper limit) = 0.086, SRMR = 0.024, ꞷ2 = 0.69)

was achieved after correlating the residuals of the last two items (keeping distance and avoiding

handshakes).

Maintaining physical hygiene was measured via five items related to washing hands and other

behaviors related to personal hygiene. A single factor structure was retained (CFI = 0.999, RMSEA

(95% CI upper limit) = 0.037, SRMR = 0.007, ꞷ2 = 0.74) with correlated residuals of the first two items

(washing hands longer and more thoroughly).

Support for COVID-19 related policy decisions was measured with five items relating to restrictive

policies affecting five areas of everyday life. A single factor structure was retained (CFI = 0.989,

RMSEA (95% CI upper limit) = 0.098, SRMR = 0.016, ꞷ2 = 0.86) after correlating residuals of support

for forbidding public gatherings and unnecessary travel, and closing parks.

Predictors

Morality

Moral identity was measured using the 10-item moral identity scale (Aquino & Reed, 2002). The

original paper reports a two-factor model (Internalization and Symbolization), with acceptable internal

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consistency. The two-factor structure was confirmed in our study after correlating residuals of items 8

and 9, and 4 and 7. (CFI = 0.939, RMSEA (95% CI upper limit) = 0.084, SRMR = 0.067, ꞷ2internalization

= 0.68, ꞷ2symbolization = 0.75).

The moral circle scale (Waytz et al., 2019) assesses the moral expansiveness across 16 different

entities (human and non-human) deemed worthy of moral concern. Participants indicated the extent

of their moral circle, i.e., the circle for which they are concerned about right and wrong done towards

them, ranging from immediate family to all things in existence.

Morality as cooperation was measured using the Relevance subscale of the Morality-as-

Cooperation Questionnaire (MAC-Q; Curry et al., 2019), which measures the extent to which each of

the seven dimensions of cooperation is relevant when making moral judgments. One item per each of

its seven dimensions was used in this study. After excluding the items of fairness and property and

correlating residuals between helping a family member and showing courage and helping a family

member and uniting a community, a general factor of the relevance of cooperation in morality (CFI =

0.991, RMSEA (95% CI upper limit) = 0.066, SRMR = 0.014, ꞷ2 = 0.73) was extracted.

(Pro)social identification and attitudes

National identity was assessed with two items combined into a scale: “I identify as [nationality]”

(Postmes et al., 2013) and “Being a [nationality] is an important reflection of who I am” (see Cameron,

2004). The correlation among items was r = 0.69, and a single score was extracted using PAF.

Social belonging was measured using a four-item single-factor scale with excellent internal

consistency (Malone et al., 2012). A single factor structure (CFI = .988, RMSEA (95% CI upper limit) =

0.115, SRMR = 0.017, ꞷ2 = 0.78) was confirmed in this study after correlating the residuals between

first and third item.

Collective (national) narcissism was measured using three items of the original, single-factor

Collective Narcissism scale (Golec de Zavala et al., 2009). Invariance of this scale was tested along

with the endorsement of COVID-19 conspiracy theories (Sternisko et al., 2021), which was

measured using a single item for a denial conspiracy and three items for deflection conspiracies (e.g.,

“a hoax invented by interest groups for financial gains”). The three items related to collective

narcissism and the four items related to belief in conspiracy theories were modeled together and

yielded a clear two-factor structure (CFI = 0.988, RMSEA (95% CI upper limit) = 0.069, SRMR =

0.021, ꞷ2Conspiracies= 0.92 and ꞷ2Collective narcissism= 0.87).

Political orientation was measured using a single item, “Overall, what would be the best description

of your political views?”, on a scale ranging from very left-leaning (“0”) to very right-leaning (“10”).

COVID-19 risk perception was measured with two items asking participants to rate how likely it was

for them and for the average person to get infected with COVID-19 by April 30, 2021, on a slider scale

ranging from 0 (“impossible”) to 100 (“certain”). Based on their high correlation (r = 0.66), a single

component was extracted using PAF.

Individual dispositions

Individual grandiose narcissism was measured using the brief version of the Narcissistic

Admiration and Rivalry Questionnaire (Back et al., 2013), comprising two subcomponents, rivalry (R)

and admiration (A), which exhibited acceptable internal consistency. The scale achieved acceptable

fit after correlating residuals between items 3 and 6 reflecting rivalry (CFI = 0.986, RMSEA (95% CI

upper limit) = 0.068, SRMR = 0.020, ꞷ2= 0.69 for admiration and ꞷ2= 0.55 for rivalry).

Trait self-control was measured as a single-factor four-item scale (Tangney et al., 2004), with the

last two items being negatively worded. However, an adequate fit was not obtained even after

correlating residuals of the first two items (CFI = 0.988, RMSEA (95% CI upper limit) = 0.115, SRMR

= 0.017, ꞷ2 = 0.78).

Self-esteem was measured using the Single-Item Self-Esteem-Scale (SISE), which achieved good

test-retest reliability and was established as a viable alternative of longer self-esteem scales (Robins

et al., 2001).

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Trait optimism was measured using two items from the three-item optimism subscale of the Life

Orientation Test-Revised (Scheier et al., 1994). Based on their high correlation (r = 0.71), a single

factor was retrieved using PAF.

Open-mindedness, reflecting the acceptance of limitation of one’s knowledge and willingness to gain

new knowledge, was measured with a six-item scale of the Multidimensional measure of intellectual

humility (Alfano et al., 2017). The originally proposed single-factor structure achieved an acceptable fit

in our study (CFI = 0.998, RMSEA (95% CI upper limit) = 0.025, SRMR = 0.007) and was retained. It

exhibited questionable internal consistency (ꞷ2 = 0.50).

Cognitive reflection was measured with a three-item test that measures the ability to inhibit intuitive

answers and engage in reflection to provide correct ones, adapted from Frederick (2005). Correct

answers were coded as “1” and incorrect as “0”, with a total scale ranging from 0 to 3.

Demographic factors and experiences

The MacArthur Scale of Subjective Social Status (Adler et al., 2000) was used to measure subjective

socio-economic status by asking participants to place themselves on an 11-rung ladder, with the top

rung representing individuals who are best off (in terms of education, jobs, and wealth), and the

bottom rung the ones worst off.

Participants were asked whether they had (coded as “1”) tested positive for COVID-19 and/or had a

close relative or acquaintance (friend, partner, family, colleague, etc.) who had tested positive for

COVID-19 (“1”) or not (“0”) by the time of data collection.

Multiple demographic factors were also collected. Participants were asked to indicate whether they

identify as “male”, “female”, or “other” and enter their age (in years). Additionally, participants’ marital

status had the following three options: married, single, in a relationship (recoded into married or in a

relationship (“1”) or other (“0”)), after which they indicated the number of children they had.

Participants were also asked to indicate their employment status (recoded into the employed,

students, or retired (“1”) or other (“0”)). Finally, participants indicated whether they lived in an urban

(coded as “1”) or rural setting (coded as “0”).

Analytical Procedure

This study was not preregistered. Our analytical approach consisted of multiple steps (see

Supplementary materials B-F) conducted in R (R Core Team, 2021). A detailed description of data

cleaning is presented in Supplementary material B, while used packages are listed at the beginning of

every Supplementary material in which they were used.

The psychometric properties of the applied measures were tested on the imputed data (see

Supplementary material C). We focused on testing the applied measures’ factor structure and internal

consistency. As the majority of the multi-item measures were taken from previously validated

instruments, CFAs with robust maximum likelihood estimator (MLR; Brosseau-Liard & Savalei, 2013;

Brosseau-Liard et al., 2012) and countries as clusters were applied using lavaan (Rosseel, 2012) to

test whether the proposed structures fit to the overall data. Modification indices were consulted when

theoretical models did not fit the data well.

We tested whether the obtained results were stable concerning the pandemic stage during data

collection. In the absence of any specific criterion, we initially attempted to group countries according

to the total number of COVID-19 cases per million inhabitants during the period of data collection,

calculated as the average of the number of cases per million at the start date of data collection and

the number of new cases per million at the end date of data collection2. In samples where only one

date was provided, we used the available information for that date. However, we noticed an unwanted

regularity in the grouping process - most countries with the total number of cases above the median

were European countries, and no countries from Africa were in this group. Thus, to minimize potential

cultural biases, we grouped the countries according to the Inglehart-Welzel cultural map (World

Values Survey 7, 2020) and selected the countries with the lowest and highest total number of cases

per million from each cultural region (Orthodox European countries, Protestant European countries,

Catholic European countries, English-Speaking countries, West & South Asian countries, Confucian

2 https://github.com/CSSEGISandData/COVID-19

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countries, African-Islamic countries, and Latin American countries) as representative. This resulted in

a group of countries in the early stage of the pandemic consisting of participants from Nigeria,

Slovakia, Australia, Bulgaria, the Philippines, and Nepal. On the other hand, a group of countries in

the advanced pandemic stage included participants from United Arab Emirates, Spain, Ireland,

Serbia, Brazil, and Singapore. Regarding Latin American countries, we considered only countries with

more than 150 participants as candidates, while no Protestant European countries were included due

to all of them being in the advanced stage of the pandemic during the data collection period (with a

total number of citizens infected per million exceeding 1000). Our two groups were highly distinctive

with respect to the total number of cases per million during the data collection (Mearly stage = 154.14;

Madvanced stage = 3520.87). In our attempt to further balance the analysis, we randomly selected the

same number of participants from each selected country, equal to the size of the smallest included

sample after the data cleaning (nUAE = 176).

Then, we checked the cross-group invariance of our multi-item measures.3 After achieving an

adequate fit by introducing changes suggested by modification indices, the cross-group invariance of

each obtained theoretical model was tested. Stepwise tests were further conducted. Firstly, configural

models were formed for each construct, followed by models with constrained item loadings to test

weak invariance, and ultimately models with constrained item loadings and intercepts to test strong

invariance. If the configural model achieved adequate fit, successive changes in fit indices with

respect to imposing restriction were used as a criterion for invariance. A CFI change of -0.015

accompanied by a change in RMSEA or SRMR of +.015 was considered as an indication of achieving

a higher level of invariance. If invariance was not achieved on the first attempt, modification indices

were consulted to achieve partial invariance. Finally, we extracted factor scores from models

reflecting strong invariance (where loadings and intercepts were constrained to form comparable

scores across countries) using the ten Berge correction to use them in further analyses.

Because two-item measures cannot be tested using CFA, factor analyses using principal axis

factoring were conducted to extract latent dimensions. In line with the factor scores based on strong

invariance, the analyses were conducted on the entire dataset used in a specific analysis.

Socio-demographic characteristics and moral circle were not scaled. Variables absent from a specific

national data set were replaced with a constant (i.e., the number of children in the Ghanaian data set

was set to median of other countries, while the residence data was coded as urban for participants

from Canada and Bulgaria).

The rest of the procedure was similar to the procedure applied by Van Lissa et al. (2022). After data

preparations, random forests were applied. Ranger function (Wright & Ziegler, 2017) was used to

apply random forests that served as a basis for partial dependence plots and permutation importance

metrics (see Altmann et al., 2010), which were used to interpret the relationships (see Supplementary

material D). Regarding the hyperparameters, the number of trees was set to 1000 and 2000, R2 was

chosen as the accuracy metric, permutation importance metrics were extracted as estimated variable

importance, the number of variables to test at each split ranged from five to twenty with an increment

of one, splitting was based on variance, while the minimum node sizes ranged from three to ninety-

nine with an increment of three. Holdout sample was used to ensure the robustness of findings: 20%

of the sample from each country formed a test set on which R2 and variable importance metrics (see

Supplementary materials E and F) were calculated.

3 We also conducted analyses with groups reflecting regions of the Inglehart-Welzel cultural map

(2020), which followed the described procedure. Due to space limitations, outputs of these analyses

can be found in Supplementary materials G.

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Results

Obtained R2 values of optimally tuned models were of weak to moderate magnitude both on the

complete data (R2contact = .134, R2hygiene = .200, R2policy = .146) and data consisting of samples

nationally representative regarding age and gender (R2contact = .172, R2hygiene = .256, R2policy = .124). In

the early stage of the pandemic, prediction of contact avoidance was negligible (R2contact = .045,

R2hygiene = .272, R2policy = .138). On the other hand, in the advanced stage of the pandemic, our models

led to a very imprecise prediction of maintaining hygiene (R2contact = .129, R2hygiene = -.043, R2policy =

.173). Therefore, we decided not to interpret the predictive contributions in models with maintaining

hygiene as the criterion on the sample reflecting the advanced stage of the pandemic. Nevertheless,

they are presented in the following paragraphs.

Figure 1. Permutation variable importance calculated with respect to representativeness of the

samples and stage of the pandemic

Results in Figure 1 show the importance metrics based on the models that yielded the highest R2 per

analysis. As permutation importance reflects a reduction in error, these plots are not directly

comparable. However, some common patterns can be observed.

In terms of avoiding contact (Figure 2), internalized moral identity provided the most consistent

contribution across analyses, followed by open-mindedness, collective narcissism, morality as

cooperation, symbolized moral identity, and self-control. Endorsement of conspiracy theories seems

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to have exhibited a stronger relationship with our criteria in the early stage of the pandemic than in the

advanced stage. In general, participants achieving higher scores on avoiding contact also achieved

higher scores on internalized moral identity, morality as cooperation, self-control, and open-

mindedness, respectively. These participants also exhibited lower endorsement of conspiracy

theories. Regarding collective narcissism and symbolized moral identity, it seems that the individuals

scoring around the midpoint reported higher contact avoidance compared to individuals scoring high

and those scoring low on the scale.

Note. Red, blue, and green represent avoiding contact, maintaining hygiene and policy support,

respectively.

Figure 2. Partial dependence plots depicting the relationships between our predictors and criteria

based on the complete data

Regarding hygiene maintenance (Figure 3), the most invariable contribution was found for social

belonging and morality as cooperation, followed by internalized and symbolized moral identity,

collective narcissism, and self-control. Gender differences in hygiene maintenance found on the

complete data and data based on representative samples were not detected in data organized

according to the stage of pandemic. Participants scoring higher on social belonging, internalized and

symbolized moral identity, collective narcissism, and self-control also scored higher on maintaining

hygiene. However, only the relationship between belonging and hygiene maintenance seemed linear

– all other lines reached a plateau at some point (usually around the midpoint), indicating participants

achieving the lowest scores on these factors also achieved the lowest scores on maintaining hygiene.

On the other hand, higher scores were related to higher reported hygiene maintenance among

participants scoring above the mean of morality as cooperation.

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Note. Red, blue, and green represent avoiding contact, maintaining hygiene and policy support,

respectively.

Figure 3. Partial dependence plots based on the data reflecting the early stage of pandemic

The most invariable predictors of policy support (Figure 4) were collective narcissism, internalized

moral identity, and self-control. Endorsement of conspiracy theories, symbolized moral identity,

possibly even morality as cooperation, and open-mindedness, seem to have exhibited a stronger

relationship with policy support in the early stages of the pandemic compared to the advanced stage.

Participants scoring higher on internalized moral identity and self-control generally were also more

supportive of policy measures. However, the relationships were not linear in the early pandemic stage

(and in the advanced stage in the context of self-control). The relationship between policy support and

collective narcissism was also complex - it was close to linear and positive in the advanced pandemic

stage, but in the early stages and on the complete data, it resembled an inverted-U-curve with a peak

around the mean. This indicates that participants scoring around the mean were most supportive of

restrictive policies, while those high and the ones low on collective narcissism were less supportive.

Participants showing more endorsement for COVID-19 conspiracy theories were less supportive,

while participants scoring higher on open-mindedness were more supportive of restrictive COVID-19

policies. The relationship between morality as cooperation and policy support was established only on

the complete data and indicated that only among those above the mean higher morality as

cooperation was related to higher policy support. The opposite was found for symbolized moral

identity – only among those lowest on this trait, the relationship between morality as cooperation and

policy support was linear and positive. No relationships were established around the mean or above

the mean.

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Note. Red, blue, and green represent avoiding contact, maintaining hygiene and policy support,

respectively.

Figure 4. Partial dependence plots based on the data reflecting the advanced stage of pandemic

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Discussion

Taking the machine learning approach, we provided several insights into social, psychological,

personality and cognitive factors in predicting COVID-19 responses. Although the nature of the

analyses (i.e., the dependence of importance estimates on error estimates which changes across

models) prevents us from direct comparisons of results across models, some consistent patterns were

observed.

Internalized moral identity was the most consistent predictor of COVID-19 attitudinal and behavioral

responses - the extent to which people perceived moral traits as central to their self-concept was

positively associated with their intentions to avoid physical contact, maintain hygiene, and support

policy measures aimed at mitigating the spread of the virus. Morality-as-cooperation was also

associated with the attitudinal and behavioral responses, most consistently in predicting hygiene

maintenance. These results suggest that maintaining hygiene, but also physical distancing and policy

support, were perceived as collective actions that benefit the group more than they benefit the self.

Symbolized moral identity was also associated with the criteria, but, interestingly, the relationship was

non-linear and strongest among participants scoring below the average of symbolized moral identity.

These findings may reflect the fact that individuals characterized by moderate or high symbolization of

moral identity prefer to be perceived as aligned with social norms, rather than actually adhering to

them (Winterich et al., 2013). However, the threshold at which the relationship becomes linear seems

to change with respect to the pandemic stage and specific criteria, indicating the need for further

research into these relationships. Overall, these findings are in line with previous research suggesting

that internalized moral identity is a relevant predictor of prosocial and cooperative intentions and

behavior, with more inconsistent results when examining the symbolization dimension of moral

identity (for a review, see Jennings et al., 2015; Winterich et al., 2013). The only variable related to

morality that did not substantially contribute to our criteria's prediction was the moral circle. Altogether,

these results indicate that morality represents an important factor in adherence to preventive

measures. Nevertheless, different aspects of morality provide different contributions to the prediction

of adherence to these measures.

Open-mindedness and self-control were positively associated with avoiding contact and supporting

policy, while self-control also exhibited a relatively steady, albeit weak, contribution to the prediction of

hygiene maintenance. Open-mindedness was conceptualized as a part of cognitive humility, which

reflects the virtue of being able to accept one’s fallibility and the willingness to accept information

contrary to one’s initial beliefs (Alfano et al., 2017; Spiegel, 2012), with some authors treating it as a

moral virtue (Arpaly, 2011; Song, 2018). Self-control is typically conceptualized as the capacity to

work effectively to reach goals, resisting short-term temptations (Fishbach & Shah 2006; Tangney et

al., 2004, ). Some authors have suggested that self-control goals are often moralized (Hofmann et al.,

2018). The relationship between open-mindedness and morality and between self-control and

morality may underlie the predictive contribution of open-mindedness and self-control established in

this study.

Social belonging was also established as a relevant predictor predominantly in terms of maintaining

hygiene, while collective narcissism also provided a substantial contribution to predicting policy

support and a less substantial contribution to predicting contact avoidance. On the one hand, ingroup

identification promotes acceptance of group norms (Livingstone et al., 2011), implying that findings on

social belonging could also reflect morality. On the other hand, the relationship between collective

narcissism and our criteria seems to be more complex, in line with the mixed evidence of previous

studies on the role of collective narcissism concerning various types of preventive behaviors such as

handwashing, physical distancing, and limiting leaving home (Nowak et al., 2020; Sternisko et al.,

2021). Namely, the evidence of a curvilinear relationship between collective narcissism and contact

avoidance and policy support might reflect the need of individuals high in collective narcissism to

establish and maintain a positive national image for the outside world (e.g., as model citizens, or

morally superior) (Cichocka, 2016; Cichocka & Cislak, 2020). However, at even higher levels of

collective narcissism, the need to assert and signal grandiosity and superiority in relation to various

threats (in this case, the virus) might manifest in lower support for restrictive preventive measures,

even at the cost of possible negative consequences for ingroup members (Cislak et al., 2018;

Marchlewska et al., 2020). This is also evident from the inverted-U curve in the context of policy

support, usually appearing slightly above the mean (except in the late stage of the pandemic, see

Figures 2 and 3). Overall, this suggests that while believing in ingroup potential may motivate

individuals to adhere to prosocial norms, irrational belief in superiority can undermine the support for

preventive measures that bring about short-term disturbance in the everyday ingroup dynamics.

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Additionally, conspiracy beliefs seem to be linked to contact avoidance and policy support, especially

in the early stage of the pandemic. Namely, endorsement of COVID-19 conspiracy theories was

associated with lower intentions to engage in physical distancing and lower policy support. Given that

conspiracy believers were found to be more self-centered (Hornsey et al., 2021) and less generous

(Alper et al., 2021) during the COVID-19 pandemic, this finding speaks in favor of viewing contact

avoidance as a form of prosocial action.

The presented findings suggest that prosociality and morality are relevant factors for understanding

physical distancing. This is in line with previous work on the role of prosociality on physical distancing

(e.g., Coroiu et al., 2020; Travaglino & Moon, 2021; Van Lissa et al., 2022; see Capraro et al. 2021 for

a review) and with the idea that personal norms, internal standards on what is right or wrong in a

given situation, play an important role in driving prosocial behavior (Capraro & Rand, 2018; Tappin &

Capraro, 2018; see Capraro & Perc, 2021, for a review). However, our results indicated a substantial

contextual variability, as well. While we focused on several most dependable and most substantial

predictors only to describe general patterns, it should be noted that multiple other factors provided a

contribution limited to a specific stage of the pandemic or specific culture (see Supplementary

materials G). This also implies that campaigns for increasing compliance with preventive measures in

future crises should be tailored to both the pandemic phase and the specifics of the culture in which

they plan to be implemented.

Generally, the obtained R2 values were lower than those reported by Van Lissa et al. (2022) in similar

analyses. In their study, injunctive norms and support for COVID-19 restrictive measures were found

to be two clearly dominant predictors of preventive behaviors, which may roughly approximate two

aspects of the Theory of Planned Behavior (Ajzen, 1991) - subjective norms and attitudes on the

specific behavior. We did not include these variables in our study, although policy support could

broadly be considered as attitudes regarding preventive measures. Conversely, we treated policy

support as one of the criteria rather than as one of the predictors, with contact avoidance, hygiene

maintenance, and policy support being moderately correlated (r = approx. .40). Thus, the simplest

explanation of the difference in the explained variance in our study compared to Van Lissa et al.

(2022) may reflect the difference in the extent to which the Theory of Planned Behavior has been

represented among predictors. Considered together, the two studies provide evidence in favor of the

Theory of Planned Behavior in the context of a global crisis.

Several limitations should be considered when interpreting our findings. Firstly, not all the national

samples were representative, and even the representative samples were not based on probabilistic

sampling, and consequently, some segments of society may have been underrepresented.

Furthermore, as the study was conducted online, our sample over-represents people with greater

access to internet-enabled technology, which may be a particularly important consideration in less-

developed countries (e.g., dissemination of conspiracy theories and fake news). Secondly, variability

in our criteria was heavily skewed in many countries (i.e., the vast majority of participants reported

high adherence to and support for preventive measures), which can be attributed to the first wave of

the pandemic during which the data were collected. Nevertheless, in some countries, data collection

was conducted during the peak of the first wave of the COVID-19 pandemic, while in other countries,

it was carried out at its beginning. Although we tried to operationalize the pandemic stage according

to the total number of infected individuals per million and took culture and sample size into account,

even such operationalization may not have eliminated all the potential sources of bias. The rough

similarity of the results based on representative and non-representative data, as well as data from

countries in different pandemic stages and data from countries grouped according to cultural zones

(see Supplementary materials G), provide arguments in favor of the validity of our findings;

nevertheless, the robustness of these more specific findings needs to be corroborated utilizing

different (i.e., longitudinal and nationally representative) samples. Furthermore, morality-as-

cooperation had to be modeled differently than proposed in the original papers to achieve invariance.

Additionally, as there are no conventional methods of testing the invariance of two-item and single-

item measures across cultures, scores on such items may be less precisely calculated than in the

case of multi-item measures. Finally, we focused on explaining variation in COVID-19 responses

without testing causality. It should be noted that we used cross-sectional, self-reported data which

may entail the desirability bias risk (Graeff, 2005). However, there is evidence that such desirability

bias does not play a key role, especially in self-reported measures like self-esteem, control, or

optimism (Caputo, 2017).

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Conclusion

Findings of our study indicate that the most effective predictors of COVID-19 responses, such as

avoiding physical contact, maintaining hygiene, and supporting restrictive COVID-19 policies, were

related to morality, prosociality, and traits and attitudes operationalizing self-control and information

processing. However, the predictive contribution of even the most invariant predictors substantially

varied with respect to the predicted type of response and cultural characteristics. While the research

design of this study prevents any causal conclusions, the results suggest that the interplay between

individual and contextual characteristics is relevant for understanding individual COVID-19 responses.

Ultimately, our findings can serve as a starting point for future, more nuanced, research on the

variables highlighted within our study. Hopefully, the growing body of research and accumulated

insights should lead to informed and efficient prevention and intervention programs for health-related

crises.

Data availability statement

The data that support the findings of this study are openly available in OSF at

http://doi.org/10.17605/osf.io/tfsza. These data have been used in multiple other manuscripts,

including the “National identity predicts public health support during a global pandemic” manuscript,

where prof. John Nezlek conducted the main analyses.

Declaration of competing interests

The authors have no competing interests to declare.

Author contributions

Conceptualization: Tomislav Pavlović and Jay van Bavel

Data curation: Tomislav Pavlović, Flavio Azevedo, Waldir M. Sampaio, Gabriel G. Rêgo, Paulo S.

Boggio

Formal analysis: Koustav De, Flavio Azevedo, Julian Riano-Moreno, Marina Maglić, Patricio Donnelly

Kehoe, César Payán-Gómez, Eugenia Hesse, Jaroslaw Kantorowicz, Steve Rathje, and Tomislav

Pavlović

Investigation (data collection): all authors

Methodology: Tomislav Pavlović and Jaroslaw Kantorowicz

Project administration: Tomislav Pavlović

Software: Koustav De, Flavio Azevedo, Julian Riano-Moreno, Marina Maglić, Patricio Donnelly

Kehoe, César Payán-Gómez, Eugenia Hesse, Jaroslaw Kantorowicz, Steve Rathje, and Tomislav

Pavlović

Supervision: Jay van Bavel, Marina Maglić, Michele Birtel, and Tomislav Pavlović

Validation: Tomislav Pavlović

Visualization: Jaroslaw Kantorowicz, Steven Rathje, and Tomislav Pavlović

Writing – original draft: Koustav De, Marina Maglić, Guanxiong Huang, Jaroslaw Kantorowicz, Michèle

D. Birtel, Philipp Schönegger, Valerio Capraro, Hernando Santamaría-García, Theofilos Gkinopoulos,

Meltem Yucel, Agustin Ibanez, Steve Rathje, Erik Wetter, Dragan T. Stanojevic, Jan-Willem van

Prooijen, Eugenia Hesse, Christian T. Elbaek, Panagiotis Mitkidis, Renata Franc, Zoran Pavlović.

Writing – review and editing: all authors

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Funding

Eva Lermer was financially supported by the VolkswagenStiftung (grant no. 98 525).

Manos Tsakiris was financially supported by the NOMIS Stiftung (NOMIS Foundation).

Ondrej Buchel was financially supported by the Slovak Research and Development Agency (SRDA)

(Agentúra na podporu výskumu a vývoja) (grant no. APVV-18-0218).

Patricia Lockwood was financially supported by the Medical Research Council (MRC) (grant no.

MR/P014097/1) and the Economic Social Research Council Impact Acceleration Award, University of

Oxford.

Michael Bang Petersen was financially supported by the Carlsberg Foundation (grant no. CF20-044).

Michael Tyrala was financially supported by the HKUST IEMS research grant project, funded by EY.

Rolf A. Zwaan was financially supported by the Netherlands Organization for Scientific Research

(NWO) (grant no. 440.20.003).

Zoran Pavlović was financially supported by the Ministry of Education, Science, and Technological

Development of the Republic of Serbia (grant no. 451-03-9/2021-14/ 200163).

Yucheng Zhang was financially supported by the National Natural Science Foundation of China (grant

no. 71972065; 71832004; 71872152), Universities in Hebei Province Hundred Outstanding Innovative

Talents Support Program (grant no. SLRC2019002); Ministry of Education in China (grant no.

21JHQ088).

Brent Strickland was financially supported by the French National Research Agency (ANR) (grant no.

ANR-10-IDEX-0001-02 PSL*, ANR-10-LABX-0087 IEC, and ANR-17-EURE-0017 FrontCog).

Jane Conway was financially supported by the IAST funding from the French National Research

Agency (ANR) under the Investments for the Future (Investissements d’Avenir) programme (grant no.

ANR-17-EURE-0010).

Matej Hruška was financially supported by the Slovak Research and Development Agency (grant no.

APVV-17-0596).

Marina Maglić was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia.

Tomislav Pavlović was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb;

Croatia.

Igor Mikloušić was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia.

Renata Franc was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia.

Agustin Ibanez was financially supported by the grants from Takeda (CW2680521); CONICET;

ANID/FONDECYT Regular (1210195 and 1210176); FONCYT-PICT 2017-1820;

ANID/FONDAP/15150012; Sistema General de Regalías (BPIN2018000100059), Universidad del

Valle (CI 5316); and the MULTI-PARTNER CONSORTIUM TO EXPAND DEMENTIA RESEARCH IN

LATIN AMERICA [ReDLat, supported by National Institutes of Health, National Institutes of Aging

(R01 AG057234), Alzheimer’s Association (SG-20-725707), Rainwater Charitable foundation - Tau

Consortium, and Global Brain Health Institute)].

Paulo Sergio Boggio was financially supported by the Coordenação de Aperfeiçoamento de Pessoal

de Nível Superior - Brasil (CAPES) (grant no. 1133/2019) and Conselho Nacional de

Desenvolvimento Científico e Tecnológico (CNPq) (grant no. 309905/2019-2).

Gabriel Gaudencio Rêgo was financially supported by the São Paulo Research Foundation (grant no.

2019/26665-5).

Waldir M. Sampaio was financially supported by the São Paulo Research Foundation - FAPESP

(grant no. 2019/27100-1).

Robert M. Ross was financially supported by the Australian Research Council (grant no.

DP180102384).

Mark Alfano was financially supported by the John Templeton Foundation (grant no. 61378).

Hans H. Tung was financially supported by the Ministry of Science and Technology, Taiwan.

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Ming-Jen Lin was financially supported by the Ministry of Science and Technology, Taiwan.

Jay Joseph Van Bavel was financially supported by the John Templeton Foundation.

Uwe Dulleck was financially supported by the QUT Centre for Behavioural Economics, Society and

Technology (BEST).

Aleksandra Cislak was financially supported by the National Science Center (grant no.

2018/29/B/HS6/02826).

Sylvie Borau was financially supported by the ANR‐Labex IAST.

F. Ceren Ay was financially supported by the Research Council of Norway through its Centres of

Excellence Scheme, FAIR project (grant no. 262675).

Becky Choma was financially supported by the Social Sciences and Humanities Research Council

(SSHRC).

Caroline Leygue was financially supported by the Sistema Nacional de Investigadores, CONACyT

(Mexico).

Raffaele Tucciarelli was financially supported by the NOMIS Foundation Distinguished Scientist

Award for the ‘Body & Image in Arts & Science’ (BIAS) Project.

Tobias Otterbring was financially supported by the Aarhus University Research Foundation (grant no.

28207).

David Stadelmann was financially supported by the Deutsche Forschungsgemeinschaft (DFG,

German Research Foundation) under Germany’s Excellence Strategy (grant no. EXC 2052/1 –

390,713,894).

Matthew A. J. Apps was financially supported by the Biotechnology and Biological Sciences Research

Council David Phillips Fellowship (grant no. BB/R010668/1).

Anton Kunnari was financially supported by the Jane & Aatos Erkko Foundation (grant no. 170112)

and the Academy of Finland (grant no. 323207).

Elena Morales-Marente was financially supported by the Contemporary Thinking and Innovation for

Social Development (COIDESO), University of Huelva, Huelva, Spain.

Teemu Saikkonen was financially supported by the Jane & Aatos Erkko Foundation (grant no.

170112) and the Academy of Finland (grant no. 323207).

Claus Lamm was financially supported by the University of Vienna, COVID-19 Rapid Response grant,

and the Austrian Science Fund (FWF, I3381).

Alexander C. Walker was financially supported by the Natural Sciences and Engineering Research

Council of Canada.

Hallgeir Sjåstad was financially supported by the Research Council of Norway, Centres of Excellence

scheme -FAIR project (grant no. 262675).

Sergio Barbosa was financially supported by the School of Medicine and Health Sciences -

Universidad del Rosario.

William Cunningham was financially supported by the SSHRC grant (grant no. 506547).

Ennio Bilancini was financially supported by the PAI2018 project PROCOPE (Prosociality, Cognition

and Peer Effects) funded by the IMT School for Advanced Studies Lucca.

Victoria H. Davis was financially supported by the SSHRC grant (grant no. 506547).

Panagiotis Mitkidis was financially supported by the Aarhus University Research Foundation (grant

no. AUFF-E-201 9-9-4).

Andrej Findor was financially supported by the Slovak Research and Development Agency (grant no.

APVV-17-0596).

Steve Rathje was financially supported by the Gates Cambridge Scholarship.

Michael J. A. Wohl was financially supported by the Social Science and Humanities Research Council

of Canada (grant no. 435-2019-0692).

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ORIGINAL UNEDITED MANUSCRIPT

Andreas Olsson was financially supported by the Swedish Research Council - Consolidator Grant

(grant no. 2018-00877).

Jonathan Fugelsang was financially supported by the Natural Sciences and Engineering Research

Council of Canada.

Luca Cian was financially supported by the Batten Institute, University of Virginia Darden School of

Business.

Raymond Boadi Frempong was financially supported by the Deutsche Forschungsgemeinschaft

(DFG, German Research Foundation) under Germany's Excellence Strategy (grant no. EXC 2052/1—

390713894).

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SARS-CoV-2 Spike Protein Exacerbates Thromboembolic Cerebrovascular Complications in Humanized ACE2 Mouse Model

Article

Full-text available

Oct 2024

COVID-19 increases the risk for acute ischemic stroke, yet the molecular mechanisms are unclear and remain unresolved medical challenges. We hypothesize that the SARS-CoV-2 spike protein exacerbates stroke and cerebrovascular complications by increasing coagulation and decreasing fibrinolysis by disrupting the renin-angiotensin-aldosterone system (RAAS). A thromboembolic model was induced in humanized ACE2 knock-in mice after one week of SARS-CoV-2 spike protein injection. hACE2 mice were treated with Losartan, an angiotensin receptor (AT1R) blocker, immediately after spike protein injection. Cerebral blood flow and infarct size were compared between groups. Vascular-contributes to cognitive impairments and dementia was assessed using a Novel object recognition test. Tissue factor-III and plasminogen activator inhibitor-1 were measured using immunoblotting to assess coagulation and fibrinolysis. Human brain microvascular endothelial cells (HBMEC) were exposed to hypoxia with/without SARS-CoV-2 spike protein to mimic ischemic conditions and assessed for inflammation, RAAS balance, coagulation, and fibrinolysis. Our results showed that the SARS-CoV-2 spike protein caused an imbalance in the RAAS that increased the inflammatory signal and decreased the RAAS protective arm. SARS-CoV-2 spike protein increased coagulation and decreased fibrinolysis when coincident with ischemic insult, which was accompanied by a decrease in cerebral blood flow, an increase in neuronal death, and a decline in cognitive function. Losartan treatment restored RAAS balance and reduced spike protein-induced effects. SARS-CoV-2 spike protein exacerbates inflammation and hypercoagulation, leading to increased neurovascular damage and cognitive dysfunction. However, the AT1R blocker, Losartan, restored the RAAS balance and reduced COVID-19-induced thromboembolic cerebrovascular complications.

Parallel electrophysiological abnormalities due to COVID‐19 infection and to Alzheimer's disease and related dementia

Article

Full-text available

Aug 2024
ALZHEIMERS DEMENT

Many coronavirus disease 2019 (COVID‐19) positive individuals exhibit abnormal electroencephalographic (EEG) activity reflecting “brain fog” and mild cognitive impairments even months after the acute phase of infection. Resting‐state EEG abnormalities include EEG slowing (reduced alpha rhythm; increased slow waves) and epileptiform activity. An expert panel conducted a systematic review to present compelling evidence that cognitive deficits due to COVID‐19 and to Alzheimer's disease and related dementia (ADRD) are driven by overlapping pathologies and neurophysiological abnormalities. EEG abnormalities seen in COVID‐19 patients resemble those observed in early stages of neurodegenerative diseases, particularly ADRD. It is proposed that similar EEG abnormalities in Long COVID and ADRD are due to parallel neuroinflammation, astrocyte reactivity, hypoxia, and neurovascular injury. These neurophysiological abnormalities underpinning cognitive decline in COVID‐19 can be detected by routine EEG exams. Future research will explore the value of EEG monitoring of COVID‐19 patients for predicting long‐term outcomes and monitoring efficacy of therapeutic interventions. Highlights Abnormal intrinsic electrophysiological brain activity, such as slowing of EEG, reduced alpha wave, and epileptiform are characteristic findings in COVID‐19 patients. EEG abnormalities have the potential as neural biomarkers to identify neurological complications at the early stage of the disease, to assist clinical assessment, and to assess cognitive decline risk in Long COVID patients. Similar slowing of intrinsic brain activity to that of COVID‐19 patients is typically seen in patients with mild cognitive impairments, ADRD. Evidence presented supports the idea that cognitive deficits in Long COVID and ADRD are driven by overlapping neurophysiological abnormalities resulting, at least in part, from neuroinflammatory mechanisms and astrocyte reactivity. Identifying common biological mechanisms in Long COVID‐19 and ADRD can highlight critical pathologies underlying brain disorders and cognitive decline. It elucidates research questions regarding cognitive EEG and mild cognitive impairment in Long COVID that have not yet been adequately investigated.

Going in, moral, circles: A data-driven exploration of moral circle predictors and prediction models

Preprint

Full-text available

Aug 2024

Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., Bayesian model exploration, Bayesian model averaging, and elastic-net regression. The study successfully replicated past research supporting the relationship between moral foundations, political orientation, and the moral circle. Additional moral psychological constructs, such as post-conventional moral reasoning and moral identity, significantly predicted the moral circle width. The identified components of the moral circle were conceptually related to phronesis, i.e., practical wisdom. We discussed the educational implications of the findings, particularly those related to moral education focusing on phronesis cultivation, multiculturalism and global citizenship, and climate justice.

SARS-CoV-2 Spike Protein Exacerbates Thromboembolic Cerebrovascular Complications in Humanized ACE2 Mouse Model

Preprint

Full-text available

Jun 2024

COVID-19 increases the risk for acute ischemic stroke, yet the molecular mechanisms are unclear and remain unresolved medical challenges. We hypothesize that the SARS-CoV-2 spike protein exacerbates stroke and cerebrovascular complications by increasing coagulation and decreasing fibrinolysis by disrupting the renin-angiotensin-aldosterone system (RAAS). A thromboembolic model was induced in humanized ACE2 knock-in mice after one week of SARS-CoV-2 spike protein injection. hACE2 mice were treated with Losartan, an angiotensin receptor (AT 1 R) blocker, immediately after spike protein injection. Cerebral blood flow and infarct size were compared between groups. Vascular-contributes to cognitive impairments and dementia was assessed using a Novel object recognition test. Tissue factor-III and plasminogen activator inhibitor-1 were measured using immunoblotting to assess coagulation and fibrinolysis. Human brain microvascular endothelial cells (HBMEC) were exposed to hypoxia with/without SARS-CoV-2 spike protein to mimic ischemic conditions and assessed for inflammation, RAAS balance, coagulation, and fibrinolysis. Our results showed that the SARS-CoV-2 spike protein caused an imbalance in the RAAS that increased the inflammatory signal and decreased the RAAS protective arm. SARS-CoV-2 spike protein increased coagulation and decreased fibrinolysis when coincident with ischemic insult, which was accompanied by a decrease in cerebral blood flow, an increase in neuronal death, and a decline in cognitive function. Losartan treatment restored RAAS balance and reduced spike protein-induced effects. SARS-CoV-2 spike protein exacerbates inflammation and hypercoagulation, leading to increased neurovascular damage and cognitive dysfunction. However, the AT 1 R blocker, Losartan, restored the RAAS balance and reduced COVID-19-induced thromboembolic cerebrovascular complications.

Knowledge, attitudes, practices, and perception of COVID-19 preventive measures among adult residents of Matadi (Democratic Republic of the Congo) after the third epidemic wave

Article

Full-text available

Jun 2024

Background Several governments from African countries, including the Democratic Republic of the Congo (DRC), implemented stringent public health measures to curb COVID-19 transmission in the early phases of the pandemic. While these restrictive measures are believed to have contributed to lowering case incidence and related mortality in DRC, data on the population’s knowledge and adherence are limited. This study aimed to assess the knowledge, perception, attitudes, and practices of COVID-19 preventive measures and associated factors among adult residents of Matadi, thereby generating evidence for a strategy adjustment as the COVID-19 response is transitioning from emergency to control status. Methods We used data from a population-based cross-sectional study conducted in October 2021. Consenting participants were enrolled through a multi-stage cluster sampling approach and administered a pre-tested structured questionnaire using a mobile application (Epicollect 5). We analyzed adult participants’ data using STATA 15.1. Univariable and multivariable analyses were applied to identify factors associated with good knowledge, good perception, positive attitude and good practice. Results We included 1,269 adult respondents for the secondary analysis. One respondent in six was female. The median age was 36 years (IQR 24–50). Most respondents (76.5%) had good knowledge. Respondents aged 40–49 years and those with vocational education level were 1.7 time (AOR 1.75, 95% CI 1.07–2.87) and twice as likely (AOR 2.06, 95% CI 1.01–4.21) to have good knowledge. Preventive measures were perceived as efficient by 45% of respondents. Good perception was associated with education level, profession, average household monthly income and good knowledge. Only 40% of respondents had a positive attitude. A positive attitude was associated with age, education level, and good knowledge. Respondents having good practice represented 5.8%. Good practice was associated with good knowledge, attitude and perception. Conclusion Most respondents were knowledgeable, had a good perception of government-related COVID-19 preventive measures, a moderately positive attitude and an extremely low level of good practice. Current COVID-19 preventive strategies, including vaccination rollout, need adjustment into high-efficiency, context-based and risk group-specific interventions. Evidence generated by this study will improve preparedness and response to future outbreaks.

Innovation for challenges of public health and sustainable growth

Preprint

May 2024

Chunsong Hu

Innovation is a vital scientific activity for improvement of human production, quality of life and economic development in the globe. This review aims to discuss innovative strategies for a series of challenges of public health and sustainable growth as well as human diseases. Currently, there are huge challenges of public health due to both major viral infectious diseases (such as HIV infection and Covid-19) and major non-communicable diseases (such as cardiovascular disease, diabetes, and cancer), as well as aging, climate change, industrialization, and urbanization. These challenges and risks greatly affect modern economic and social development not only in China but also in the globe. Herein, we should “know more, do better, and be smarter” about related knowledge and information, “reject misinformation and accept uncertainty”, and further understand the important roles of policies and laws as agents (PLA) in fighting against the infection of SARS-CoV-2 and its major variants, and effectively halting climate change for global health and sustainable growth. All in all, these innovative strategies and experiences in combating Covid-19 infection and climate change will help to get better economic and social development as well as sustainable growth in the countries worldwide, in particular the Covid-19 pandemic and post-COVID-19 era.

How Political Ideology and Media Shaped Vaccination Intention in the Early Stages of the COVID-19 Pandemic in the United States

Article

Full-text available

May 2024

As a pharmaceutical intervention, vaccines remain a major public health strategy for mitigating the effects of COVID-19. Yet, vaccine intake has been affected by various cognitive and cultural factors. We examine how a selected set of factors (i.e., knowledge, concern, media, peer influence, and demographics) shaped COVID-19 vaccination intention in the early phase of the pandemic (Fall 2020). Using a survey conducted in three US states (Idaho, Texas, and Vermont) just prior to the rollout of the first vaccines against COVID-19, we find that COVID-19 concern was the primary driver of vaccination intention. Concern was shaped mainly by two factors: political ideology and media sources. Yet, ideology and media were much more important in affecting concern for those who leaned politically conservative, as opposed to those who leaned liberal or remained moderate. The results from our structural equation models affirm that the information politically conservative respondents were receiving reinforced the effects of their ideology, leading to a greater reduction in their concern. We discuss the potential implications of these findings for future pandemic preparedness.

The environment is somewhat alike for different adaptive motivations: Machine learning reveals optimal motivational contexts involve collective support of parents, teachers, and peers

Article

Nov 2024
CONTEMP EDUC PSYCHOL

Achievement motivation is fundamental for human flourishing. While numerous adaptive motivational constructs have been proposed, they are often examined in isolation without considering their shared contextual roots. To identify the contextual factors underlying different forms of adaptive achievement motivation, we conducted comprehensive analyses by integrating a global student assessment dataset (n = 77,068 middle-school students across 19 countries, Mage = 15.79). We conducted a literature review and identified 27 potential predictors theoretically and empirically related to achievement motivation, including immediate contextual factors available in the dataset and distal contextual factors available from varying sources. Results from machine learning analyses showed convergent patterns of the contextual predictors for adaptive motivation (self-efficacy, learning goals, and task mastery orientation). Specifically, the optimal environment for adaptive motivation is characterized by the collective positive influence from parents, teachers, and peers, rather than depending on one exclusively. In comparison, the pattern of other less adaptive motivation constructs (fixed mindsets, performance goals, and fear of failure) is idiosyncratic. These findings provide synthesized evidence consolidating achievement motivation research, highlighting the shared contextual foundations for various adaptive motivations. This integrative approach clarifies that the optimal motivational contexts involve the collective social support of parents, teachers, and peers.

Attraction by pairwise coherence explains the emergence of ideological sorting

Article

Jul 2024

Political polarization has become a growing concern in democratic societies, as it drives tribal alignments and erodes civic deliberation among citizens. Given its prevalence across different countries, previous research has sought to understand under which conditions people tend to endorse extreme opinions. However, in polarized contexts, citizens not only adopt more extreme views but also become correlated across issues that are, a priori, seemingly unrelated. This phenomenon, known as “ideological sorting”, has been receiving greater attention in recent years but the micro-level mechanisms underlying its emergence remain poorly understood. Here, we study the conditions under which a social dynamic system is expected to become ideologically sorted as a function of the mechanisms of interaction between its individuals. To this end, we developed and analyzed a multidimensional agent-based model that incorporates two mechanisms: homophily (where people tend to interact with those holding similar opinions) and pairwise-coherence favoritism (where people tend to interact with ingroups holding politically coherent opinions). We numerically integrated the model’s master equations that perfectly describe the system’s dynamics and found that ideological sorting only emerges in models that include pairwise-coherence favoritism. We then compared the model’s outcomes with empirical data from 24,035 opinions across 67 topics and found that pairwise-coherence favoritism is significantly present in datasets that measure political attitudes but absent across topics not considered related to politics. Overall, this work combines theoretical approaches from system dynamics with model-based analyses of empirical data to uncover a potential mechanism underlying the pervasiveness of ideological sorting.

Using machine learning to unveil relevant predictors of adherence to recommended health-protective behaviors during the COVID-19 pandemic in Denmark

Article

Jun 2024

What were relevant predictors of individuals' proclivity to adhere to recommended health‐protective behaviors during the COVID‐19 pandemic in Denmark? Applying machine learning (namely, lasso regression) to a repeated cross‐sectional survey spanning 10 months comprising 25 variables (Study 1; N = 15,062), we found empathy toward those most vulnerable to COVID‐19, knowledge about how to protect oneself from getting infected, and perceived moral costs of nonadherence to be strong predictors of individuals' self‐reported adherence to recommended health‐protective behaviors. We further explored the relations between these three factors and individuals' self‐reported proclivity for adherence to recommended health‐protective behaviors as they unfold between and within individuals over time in a second study, a Danish panel study comprising eight measurement occasions spanning eight months ( N = 441). Results of this study suggest that the relations largely occurred at the trait‐like interindividual level, as opposed to at the state‐like intraindividual level. Together, the findings provide insights into what were relevant predictors for individuals' overall level of adherence to recommended health‐protective behaviors (in Denmark) as well as how these predictors might (not) be leveraged to promote public adherence in future epidemics or pandemics.

Cooperation and Acting for the Greater Good During the COVID-19 Pandemic

Chapter

Full-text available

Jan 2024

The book begins by overviewing the timeline of the pandemic and how it affected life, followed by a discussion of the ethics and legal aspects of the pandemic. It then discusses behaviors during the pandemic (e.g., social distancing, protesting) before discussing experiences during the pandemic (e.g., prejudice, well-being, stress, joblessness, family changes). Next, the book discusses outcomes after the pandemic (e.g., global outcomes, societal outcomes, and individual outcomes; these outcomes might be related to shifts in the economy, health, morality, politics). To compliment these theory-based sections, Section Five discusses best practices for conducting future studies during or about the pandemic.

Social and moral psychology of COVID-19 across 69 countries

Preprint

Full-text available

May 2022

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behavior change. To help scholars better understand the social and moral psychology behind public health behavior, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of individual differences and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

Article

Full-text available

Mar 2022

Before vaccines for COVID-19 became available, a set of infection prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection prevention behavior in 56,072 participants across 28 countries, administered in March-May 2020. The machine-learning model predicted 52% of the variance in infection prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically-derived predictors were relatively unimportant.

National identity predicts public health support during a global pandemic

Article

Full-text available

Jan 2022

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.

Comparing Responses in Repeated Cross-Sectional and Panel Studies: Results Across Eight Weeks During the First COVID-19 Lockdown in Denmark

Article

Full-text available

Jul 2021

It is often important to study people’s thoughts, feelings, and behaviors over time. To this end, researchers have relied on repeated cross-sectional studies, in which different people from the same population participate on different measurement occasions. Also, researchers have relied on panel studies, in which the same group of people participate on different measurement occasions. However, few studies have directly tested whether participants’ responses in repeated cross-sectional studies were similar to those found in panel studies. To address this gap, we compared the responses to 33 items, 28 of which were further grouped into four aggregates (Affections, Worries, States, Health concerns), over eight weeks during the first COVID-19 lockdown in Denmark in a repeated cross-sectional study (overall N = 5,993, per measurement occasion 616 ≤ n ≤ 964) with the responses in a largely equivalent panel study (993 ≤ n ≤ 2,546 across measurement occasions). The study participants were randomly drawn from the same quota-representative participant pool and responded to the same items on the same measurement occasions. Results indicated a few differences between the study samples on the first measurement occasion (i.e., selection effects between studies). Further, we found statistical support for different trajectories in 21 aggregates/items. However, visual inspection of the trajectories suggested subtle differences between the studies at large. The results thus raise awareness that the trajectories of people’s thoughts, feelings, and behaviors can differ between survey methods, especially when only a few measurement occasions are considered. Nevertheless, such differences might not be substantial over time.

A Psychological Network Approach to Attitudes and Preventive Behaviors During Pandemics: A COVID-19 Study in the United Kingdom and the Netherlands

Article

Full-text available

Mar 2021

Preventive behaviors are crucial to prevent the spread of the coronavirus causing COVID-19. We adopted a complex psychological systems approach to obtain a descriptive account of the network of attitudes and behaviors related to COVID-19. A survey study ( N = 1,022) was conducted with subsamples from the United Kingdom ( n = 502) and the Netherlands ( n = 520). The results highlight the importance of people’s support for, and perceived efficacy of, the measures and preventive behaviors. This also applies to the perceived norm of family and friends adopting these behaviors. The networks in both countries were largely similar but also showed notable differences. The interplay of psychological factors in the networks is also highlighted, resulting in our appeal to policy makers to take complexity and mutual dependence of psychological factors into account. Future research should study the effects of interventions aimed at these factors, including effects on the network, to make causal inferences.

Compliance and Self-Reporting During the COVID-19 Pandemic: A Cross-Cultural Study of Trust and Self-Conscious Emotions in the United States, Italy, and South Korea

Article

Full-text available

Mar 2021

The coronavirus COVID-19 pandemic is an unprecedented health crisis. Many governments around the world have responded by implementing lockdown measures of various degrees of intensity. To be effective, these measures must rely on citizens’ cooperation. In the present study, we drew samples from the United States (N = 597), Italy (N = 606), and South Korea (N = 693) and examined predictors of compliance with social distancing and intentions to report the infection to both authorities and acquaintances. Data were collected between April 6th and 8th 2020. We investigated the role of cultural orientations of horizontal and vertical individualism and collectivism, self-conscious emotions of shame and guilt related to the infection and trust in the government’s action. Across all countries, vertical collectivism (VC) predicted stronger shame, whereas horizontal collectivism predicted stronger trust in the government. Only in the United States, VC was associated with stronger trust. Stronger feelings of shame predicted lower compliance and intentions to report the infection to both authorities and acquaintances. In contrast, guilt was associated with stronger intentions to report the infection to the authorities. Finally, trust was associated with stronger compliance and intentions to report the infection to the authorities. Unlike Italy and South Korea, the association between trust on compliance was not statistically significant in the United States, implications of the findings, and directions for future research are discussed.

Mathematical foundations of moral preferences

Article

Full-text available

Feb 2021
J R SOC INTERFACE

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary pay-offs of others. However, during the last 10 years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment and trustworthiness are in fact better explained by preferences for following one's own personal norms-internal standards about what is right or wrong in a given situation. Beyond better organizing various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.

National Narcissism predicts the Belief in and the Dissemination of Conspiracy Theories During the COVID-19 Pandemic: Evidence From 56 Countries

Article

Dec 2021

Conspiracy theories related to coronavirus disease 2019 (COVID-19) have propagated around the globe, leading the World Health Organization to declare the spread of misinformation an “Infodemic.” We tested the hypothesis that national narcissism—a belief in the greatness of one’s nation that requires external recognition—is associated with the spread of conspiracy theories during the COVID-19 pandemic. In two large-scale national surveys ( N Total = 950) conducted in the United States and the United Kingdom, and secondary analysis of data from 56 countries ( N = 50,757), we found a robust, positive relationship between national narcissism and proneness to believe and disseminate conspiracy theories related to COVID-19. Furthermore, belief in COVID-19 conspiracy theories was related to less engagement in health behaviors and less support for public-health policies to combat COVID-19. Our findings illustrate the importance of social identity factors in the spread of conspiracy theories and provide insights into the psychological processes underlying the COVID-19 pandemic.

To what extent are conspiracy theorists concerned for self versus others? A COVID‐19 test case

Article

Feb 2021

We examined whether people who are prone to believe COVID‐19 conspiracy theories are characterised by an especially strong concern for others or an especially strong concern for the self, and whether these orientations are associated with willingness to take a COVID‐19 vaccine. We surveyed 4,245 participants from eight nations; three months later we re‐contacted 1,262 participants from three nations. Belief in COVID‐19 conspiracy theories was related to greater concerns about one’s own safety, and lower concerns about the safety of close others. Furthermore, conspiracist ideation at Wave 1 predicted reluctance to take a COVID‐19 vaccine at Wave 2, mediated through relative concern for self versus other. In sum, people who are high in conspiracy beliefs have relatively higher concern for the self relative to others, with troubling implications for public health.