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Article

How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms

1
Department of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy
2
Department of General Psychology, University of Padova, 35131 Padova, Italy
3
Department of Dynamic and Clinical Psychology, Sapienza University of Rome, 00185 Rome, Italy
4
Department of Medical Surgical Science and Translational Medicine, Sapienza University of Rome, 00189 Rome, Italy
5
Department of Neuroscience, Imaging and Clinical Sciences, University “G.d’Annunzio”, 66100 Chieti-Pescara, Italy
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(19), 7252; https://doi.org/10.3390/ijerph17197252
Received: 2 September 2020 / Revised: 29 September 2020 / Accepted: 30 September 2020 / Published: 4 October 2020
(This article belongs to the Special Issue The COVID-19 Pandemic in Europe: Response to Challenges)
In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18–22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82–0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance. View Full-Text
Keywords: COVID-19; compliance; efficacy; risk perception; civic engagement; personality COVID-19; compliance; efficacy; risk perception; civic engagement; personality
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MDPI and ACS Style

Roma, P.; Monaro, M.; Muzi, L.; Colasanti, M.; Ricci, E.; Biondi, S.; Napoli, C.; Ferracuti, S.; Mazza, C. How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms. Int. J. Environ. Res. Public Health 2020, 17, 7252. https://doi.org/10.3390/ijerph17197252

AMA Style

Roma P, Monaro M, Muzi L, Colasanti M, Ricci E, Biondi S, Napoli C, Ferracuti S, Mazza C. How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms. International Journal of Environmental Research and Public Health. 2020; 17(19):7252. https://doi.org/10.3390/ijerph17197252

Chicago/Turabian Style

Roma, Paolo, Merylin Monaro, Laura Muzi, Marco Colasanti, Eleonora Ricci, Silvia Biondi, Christian Napoli, Stefano Ferracuti, and Cristina Mazza. 2020. "How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms" International Journal of Environmental Research and Public Health 17, no. 19: 7252. https://doi.org/10.3390/ijerph17197252

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