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A platform for research: civil engineering, architecture and urbanism
Using an integrated social cognition model to predict COVID‐19 preventive behaviours
Objectives Rates of novel coronavirus disease 2019 (COVID‐19) infections have rapidly increased worldwide and reached pandemic proportions. A suite of preventive behaviours have been recommended to minimize risk of COVID‐19 infection in the general population. The present study utilized an integrated social cognition model to explain COVID‐19 preventive behaviours in a sample from the Iranian general population. Design The study adopted a three‐wave prospective correlational design. Methods Members of the general public (N = 1,718, M age = 33.34, SD = 15.77, male = 796, female = 922) agreed to participate in the study. Participants completed self‐report measures of demographic characteristics, intention, attitude, subjective norm, perceived behavioural control, and action self‐efficacy at an initial data collection occasion. One week later, participants completed self‐report measures of maintenance self‐efficacy, action planning and coping planning, and, a further week later, measures of COVID‐19 preventive behaviours. Hypothesized relationships among social cognition constructs and COVID‐19 preventive behaviours according to the proposed integrated model were estimated using structural equation modelling. Results The proposed model fitted the data well according to multiple goodness‐of‐fit criteria. All proposed relationships among model constructs were statistically significant. The social cognition constructs with the largest effects on COVID‐19 preventive behaviours were coping planning (β = .575, p < .001) and action planning (β = .267, p < .001). Conclusions Current findings may inform the development of behavioural interventions in health care contexts by identifying intervention targets. In particular, findings suggest targeting change in coping planning and action planning may be most effective in promoting participation in COVID‐19 preventive behaviours. ; peerReviewed
Using an integrated social cognition model to predict COVID‐19 preventive behaviours
Objectives Rates of novel coronavirus disease 2019 (COVID‐19) infections have rapidly increased worldwide and reached pandemic proportions. A suite of preventive behaviours have been recommended to minimize risk of COVID‐19 infection in the general population. The present study utilized an integrated social cognition model to explain COVID‐19 preventive behaviours in a sample from the Iranian general population. Design The study adopted a three‐wave prospective correlational design. Methods Members of the general public (N = 1,718, M age = 33.34, SD = 15.77, male = 796, female = 922) agreed to participate in the study. Participants completed self‐report measures of demographic characteristics, intention, attitude, subjective norm, perceived behavioural control, and action self‐efficacy at an initial data collection occasion. One week later, participants completed self‐report measures of maintenance self‐efficacy, action planning and coping planning, and, a further week later, measures of COVID‐19 preventive behaviours. Hypothesized relationships among social cognition constructs and COVID‐19 preventive behaviours according to the proposed integrated model were estimated using structural equation modelling. Results The proposed model fitted the data well according to multiple goodness‐of‐fit criteria. All proposed relationships among model constructs were statistically significant. The social cognition constructs with the largest effects on COVID‐19 preventive behaviours were coping planning (β = .575, p < .001) and action planning (β = .267, p < .001). Conclusions Current findings may inform the development of behavioural interventions in health care contexts by identifying intervention targets. In particular, findings suggest targeting change in coping planning and action planning may be most effective in promoting participation in COVID‐19 preventive behaviours. ; peerReviewed
Using an integrated social cognition model to predict COVID‐19 preventive behaviours
Lin, Chung‐Ying (author) / Imani, Vida (author) / Majd, Nilofar Rajabi (author) / Ghasemi, Zahra (author) / Griffiths, Mark D. (author) / Hamilton, Kyra (author) / Hagger, Martin S. (author) / Pakpour, Amir H. (author)
2020-01-01
Article (Journal)
Electronic Resource
English
DDC:
710
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