Factors associated with preventive behaviours of COVID-19 among hospital staff in Iran in 2020: an application of the Protection Motivation Theory

Bashirian, Saeed; Jenabi, Ensieh; Khazaei, Salman; Barati, Majid; Karimi‐Shahanjarini, Akram; Zareian, Sepideh; Rezapur-Shahkolai, Forouzan; Moeini, Babak

doi:10.1016/j.jhin.2020.04.035

Cited by 217 publications

(219 citation statements)

References 7 publications

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“…Stated differently, our findings suggest that individuals who perceive themselves as able to carry out (i.e., those with self-efficacy) those behaviors judged as effective in reducing the threat (i.e., behaviors with perceived efficacy) are more likely to comply with the government measures. This finding highlights the key role of self-efficacy in the adoption and maintenance of recommended health actions [ 21 ] and suggests a relationship between self-efficacy and compliance with the preventive measures during the COVID-19 pandemic, as previously reported for different populations (e.g., healthcare workers) [ 68 , 69 ]. Furthermore, some covariates were also significant: older age, lower education levels, and lower levels of personality dysfunction were all associated with increased compliance.…”

Section: Discussionsupporting

confidence: 78%

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

Roma

Monaro

Muzi

et al. 2020

IJERPH

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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.

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Section: Discussionsupporting

confidence: 78%

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

Roma

Monaro

Muzi

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…In the present research, protective behaviors showed to be at a desirable level, which is consistent with some research by Barati et al about protective behaviors among hospital staff (34). Moreover, in the present research among preventive behaviors, avoiding unnecessary travel was found to be the most prevalent protective behavior, which seems to result from limits set by social distancing.…”

Section: Resultssupporting

confidence: 92%

“…A statistically signi cant positive correlation was also observed between perceived vulnerability and severity of the disease. This nding is consistent with a number of studies including Barati et al (34)., Zare et al (21) and Park et al (35). These ndings show that for a better perception of the risk of a iction with the disease, a higher level of perceived vulnerability is needed, as such a perception can strongly and positively affect the perceived threat of COVID-19.…”

Section: Resultssupporting

confidence: 90%

Predicting COVID-19 Preventive Behaviors based on Protection Motivation Theory in Hormozgan, Iran

Rad

Takhti

Azad

et al. 2020

Preprint

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Background: The present research aimed to predict the preventive behaviors of COVID-19 in the light of the Protection Motivation Theory (PMT).Methods: The present cross-sectional research was conducted in 2 months (March 2020-April 2020) in Hormozgan Province of Iran. The research population comprised all residents above 15 years of age. They received an online questionnaire survey in two parts, demographic information along with PMT constructs. To analyze the data, independent t-test and one-way anova test, Multiple linear regression and the statistical technique of path analysis and structural equation modeling was used. All statistical calculations and hypothesis testing were performed using spss21 and Amos21 software and a significant level of hypothesis testing was considered to be 0.05Results: A total number of 2,032 subjects participated in this research, the mean age of whom was 34.84±9.8 (R=15-98). The majority of participants were between 31 - 40 years old, female (60.4%), married (72%), urban residents (87.3%), holding a Bachelor’s degree or higher (58.8%) and employed (58.8%). Statistically significant positive correlation coefficients were estimated between preventive behaviors of COVID-19 and perceived vulnerability (r=0.192, p<0.001), perceived severity (r=0.092, p<0.001), response efficacy (r=0.398, p<0.001) ,self efficacy (r=0.497, p<0.001) and protection motivation (r=0.595, p<0.001). Statistically significant negative correlations were found between behavior and maladaptive behavior rewards (r=-0.243, p<0.001) and perceived costs (r=-0.121, p<0.001). Conclusions: The present findings showed that maladaptive behavior reward and fear predicted protective behaviors negatively; response efficacy and self-efficacy predicted protective behaviors positively. The effect of self-efficacy was the strongest among all. The information provided in this research can contribute to policy-making in the country.

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“…In the present research, protective behaviors showed to be at a desirable level, which is consistent with some research by Barati et al about COVID-19 protective behaviors among hospital staff (34). Moreover, in the present research among preventive behaviors, avoiding unnecessary travel was found to be the most prevalent protective behavior, which seems to result from limits set by social distancing.…”

Section: Discussionsupporting

confidence: 91%

Predicting COVID-19 Preventive Behaviors based on Protection Motivation Theory in Hormozgan, Iran.

Rad

Takhti

Azad

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The present research aimed to predict the preventive behaviors of COVID-19 in the light of the Protection Motivation Theory (PMT).Methods: The present cross-sectional research was conducted in 2 months (March 2020-April 2020) in Hormozgan Province of Iran. The research population comprised all residents above 15 years of age. They received an online questionnaire survey in two parts, demographic information along with PMT constructs. To analyze the data, independent t-test and one-way anova test, Multiple linear regression and the statistical technique of path analysis and structural equation modeling was used. All statistical calculations and hypothesis testing were performed using spss21 and Amos21 software and a significant level of hypothesis testing was considered to be 0.05Results: A total number of 2,032 subjects participated in this research, the mean age of whom was 34.84±9.8 (R=15-98). The majority of participants were between 31 - 40 years old, female (60.4%), married (72%), urban residents (87.3%), holding a Bachelor’s degree or higher (58.8%) and employed (58.8%). Statistically significant positive correlation coefficients were estimated between preventive behaviors of COVID-19 and perceived vulnerability (r=0.192, p<0.001), perceived severity (r=0.092, p<0.001), response efficacy (r=0.398, p<0.001) ,self efficacy (r=0.497, p<0.001) and protection motivation (r=0.595, p<0.001). Statistically significant negative correlations were found between behavior and maladaptive behavior rewards (r=-0.243, p<0.001) and perceived costs (r=-0.121, p<0.001).Conclusions: The present findings showed that maladaptive behavior reward and fear predicted protective behaviors negatively; response efficacy and self-efficacy predicted protective behaviors positively. The effect of self-efficacy was the strongest among all. The information provided in this research can contribute to policy-making in the country.

show abstract

Factors associated with preventive behaviours of COVID-19 among hospital staff in Iran in 2020: an application of the Protection Motivation Theory

Cited by 217 publications

References 7 publications

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

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

Predicting COVID-19 Preventive Behaviors based on Protection Motivation Theory in Hormozgan, Iran

Predicting COVID-19 Preventive Behaviors based on Protection Motivation Theory in Hormozgan, Iran.

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Factors associated with preventive behaviours of COVID-19 among hospital staff in Iran in 2020: an application of the Protection Motivation Theory

Cited by 217 publications

References 7 publications

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

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

Predicting COVID-19 Preventive Behaviors based on&nbsp;Protection Motivation Theory in Hormozgan, Iran

Predicting COVID-19 Preventive Behaviors based on Protection Motivation Theory in Hormozgan, Iran.

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Predicting COVID-19 Preventive Behaviors based on Protection Motivation Theory in Hormozgan, Iran