14 research outputs found
Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
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 multinational 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 results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible 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.Peer reviewe
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Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
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 multinational 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 results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible 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
Author Correction: National identity predicts public health support during a global pandemic
Correction to: Nature Communications https://doi.org/10.1038/s41467-021-27668-9, published online 26 January 2022
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Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
Funder: Institute of Social Sciences Ivo Pilar, Zagreb; CroatiaFunder: CONICET; DOI: https://doi.org/10.13039/501100002923Funder: National Institutes of Health; DOI: https://doi.org/10.13039/100000002Funder: Ministry of Science and Technology; DOI: https://doi.org/10.13039/100007225Funder: QUT Centre for Behavioural Economics, Society and TechnologyFunder: Sistema Nacional de Investigadores; DOI: https://doi.org/10.13039/501100013395Funder: CONACYT; DOI: https://doi.org/10.13039/501100003141Funder: NOMIS Foundation; DOI: https://doi.org/10.13039/501100008483Funder: University of Huelva; DOI: https://doi.org/10.13039/501100005604Funder: University of Vienna; DOI: https://doi.org/10.13039/501100003065Funder: Natural Sciences and Engineering Research Council of Canada; DOI: https://doi.org/10.13039/501100000038Funder: Universidad del Rosario; DOI: https://doi.org/10.13039/501100008793Funder: Batten Institute; DOI: https://doi.org/10.13039/100008596Funder: University of Virginia Darden School of Business; DOI: https://doi.org/10.13039/100008193At 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 multinational 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 results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible 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
Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
Social and moral psychology of COVID-19 across 69 countries
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
Author Correction: National identity predicts public health support during a global pandemic
In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected