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W4205656441 abstract "<sec> <title>BACKGROUND</title> In the wake of the sudden spread of COVID-19, much of the Italian population practiced behaviors that were incongruous with the protective health measures promoted by the Italian government. </sec> <sec> <title>OBJECTIVE</title> The present study aimed at examining psychological and psychosocial variables that could potentially predict behavioral compliance. </sec> <sec> <title>METHODS</title> An online survey was administered from 18–22 March 2020. There were 2,766 participants (71.7% female, 28.3% male), with an average age of 32.94 years (SD = 13.2; range 18–90 years). 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 on all of the collected psychosocial variables to predict which individuals would be more likely to comply with COVID-19 protective measures. </sec> <sec> <title>RESULTS</title> The results indicated significantly lower scores in behavioral compliance (M = 41.7, SD = 6.20) relative to efficacy perception (M = 44.8, SD = 6.17). The introduction of risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. The impact of perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The best pool of predictors (15 out of 199), identified using the correlation-based feature selector, produced a ROC area in the range of 0.83–0.93 for classifying individuals as high versus low compliance. </sec> <sec> <title>CONCLUSIONS</title> Government awareness communications and campaigns regarding COVID-19 and related protective measures should be tailored to specific segments of the population, as defined by age and level of education. Furthermore, they should emphasize the efficacy of the recommended measures in successfully preventing the virus spread. Finally, they should take into account risk perception and should highlight the importance of civic engagement. </sec> <sec> <title>CLINICALTRIAL</title> N/A </sec>" @default.
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W4205656441 date "2020-06-10" @default.
W4205656441 modified "2023-10-16" @default.
W4205656441 title "How to Improve Compliance with Protective Health Measures During the Covid-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms (Preprint)" @default.
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W4205656441 doi "https://doi.org/10.2196/preprints.21249" @default.
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