Skip to main content
Download PDF

Determine the factors that affected COVID-19 prevention behaviors based on constructs of social cognition theory

BMC Public Health volume 23, Article number: 2312 (2023) Cite this article

Abstract

Background

COVID-19 is a universal challenge and novel disease is one of the core public health concerns. This study aimed to determine the factors that affected COVID-19 prevention behaviors (CPB) based on constructs of Social Cognition Theory (SCT).

Methods

This cross-sectional study was conducted on 426 participants referred to health centers of Arak from October 2021 to February 2022, and they were selected through a multi-stage random sampling method. Data were collected via a self-administered questionnaire which includes socio-demographic data (6 items), COVID-19 prevention behaviors (12 items), and SCT constructs related to COVID-19 (32 items). SPSS Ver-16 statistical software was used to analyze the data with one-way ANOVA, independent samples t-test, and multiple linear regressions. The significance level of α = 0.05 was considered for all tests.

Results

The mean age was 37.8 years (SD = 12.2) and ranged from 17 to 81. According to the results, 51.2% had higher education and 52.6% of the participants were female. The mean (SD) of COVID-19 preventive behaviors was 43 (SD = 7.8) out of 60. There was statistical association between CPB and three constructs of SCT. Multiple regression showed that the outcomes expectations (β = 0.11, p < 0.001), self-regulation (β = 0.41, p < 0.001), and self-efficacy (β = 0.30, p < 0.001), age, gender, and history of COVID 19infection were associated with CPB and those described 61% of the variance of CPB.

Conclusion

Rendering to the result of this study constructs of SCT is the key predictor of participants’ CPB. Hence, based on these predictors, effective interventions and healthy messages could be designed based on this predictor—outcomes expectations, self-regulation, social support, and self-efficacy which can be beneficial to improve healthy behavior.

Peer Review reports

Introduction

A new contagious disease was reported in December 2019 and the World Health Organization (WHO) called it COVID-19 on 30 January 2020 [1]. COVID-19 infection, as a universal challenge and novel disease, is one of the core public health concerns [2]. In the last report of WHO on 11 May 2023, globally about 2.7 million new cases of COVID-19 and over 17,000 deaths were reported in 28-day intervals from 10 April to 7 May 2023. Globally over 765 million suffered from COVID-19 and over 6.9 million deaths have been reported. At the six regionals’ WHO, 40,000 new cases were reported in the Eastern Mediterranean in the last 28 days in the last report on 11 May 2023. In this report, the highest numbers of new cases (12 023; 14.3 per 100 000) and new deaths (595; 0.7 per 100 000) were reported in Iran [3].

Toward prevention and surveillance of COVID-19 WHO has delivered numerous guidelines about this situation. It spreads generally from person to person via respiratory droplets, sneezing, cough talk, shouting, or singing [4]. Literature and documents are recommended vaccination and guidelines for COVID-19 prevention behaviors [1, 5, 6]. Despite the recommendation for COVID-19 prevention behaviors, it is inadequate [7, 8] and some studies showed that individuals are against COVID-19 prevention behaviors [9, 10].

Sociologists and psychologists have recommended the use of theories and models to explain the factors affecting health behaviors. Behavioral theories help to better understand the complexity of the factors affecting health behavior and reinforcing educational interventions [11, 12]. Studies designed based on a model or theory can better measure the factors affecting the behavior and determine the reasons for the success or failure of health-related behaviors [13, 14]. It is better to use behavioral theories when you need to know why persons do not follow COVID-19 preventive behaviors and the impact of various factors on the acceptance of behavior [15, 16]. A conceptual review study (2022) on social cognition theories and behavior change in COVID-19″- suggests that apply SCT to predict the controlling of COVID-19 as an endemic disease, booster vaccination. This study also suggested conducting more research using the SCT to determine the main factor affecting the preventive behavior of COVID-19 [17]. The literature recommends that the SCT can be applied to understand adherence to COVID-19 prevention practices [8, 16,17,18]. Social cognitive theory in 1963 by Bandura was introduced. According to this theory, behavior is described through three factors including individual, behavioral, and environmental or interpersonal social factors. The core elements and key SCT constructs of including outcome expectations, self-regulation, self-efficacy, and social support [19].

We will not need a theory if we know the rate of COVID-19 or the rate of adherence to COVID-19 prevention behaviors. However, when we want to know the impact of various factors on the adoption of healthy behavior, such as why people do not wear masks or why they do not follow COVID-19 prevention behaviors, we will need to use behavioral theory to better explain and understand it.With considering the difference in numerous reports as well as the less use of social cognitive theory studies and the need to investigate the behavior of the people of a community regarding infectious respiratory diseases in the event of outbreaks similar to the Covid-19 disease in the post-corona era were pointed out as existing information gaps in introduction section. Therefore this study aimed to determine the factors affected by COVID-19 prevention behaviors (CPB) based on constructs of the SCT.

Method

Study design and population

This cross-sectional study was directed at 426 participants who were referred to the health centers of Arak, from August 2021 to November 2022. Arak is located in central Iran and the center of Markazi province a population of around 600,000.

Sample size estimation

The sample size was considered 430 based on a 40.9% prevalence of COVID-19 prevention behaviors established in a previous study, with a level of confidence (zα/2 = 1.96), 5% level of precision, and non-response rate (estimated to be 20%). Using the following formula:

$$n=\frac{{Z}^{2}\times p\left(1-p\right)}{{d}^{2}}=426$$

The multi-stage stratified method was used for sample selection. Two health centers were randomly selected from five regions according to socioeconomic status. Then, at each health center eligible participants were randomly selected finally, 426 eligible participants were selected from 10 health centers from five districts of Arak.

The inclusion criteria were willingness to participate in the study and not suffering from special mental and emotional diseases.

Measuring tools

Data were collected by a self-administered questionnaire which includes socio-demographic data (6 items), COVID-19 prevention behaviors (12 items), and SCT constructs related to COVID-19 (32 items). Data were collected through self-administered and completing the questionnaire took nearly 20 min. The strengthening of the reporting of observational studies in epidemiology (STROBE) statement was applied in this study.

Socio-demographic data

Socio-demographic data comprised gender, age, education level (primary, secondary, high school, diploma, and academic), occupation and marital status, and economic status (poor, average, good).

COVID-19 prevention behaviors

The COVID-19 prevention behaviors were measured with 12 items. For example, the frequency of mask use was asked and the given score ranged from 1 (never) to 5 (always).

SCT constructs related to COVID-19

The SCT constructs related to COVID-19 were assessed through 32 items derived from the available literature according to the SCT procedures and guidelines [12, 19]. The SCT constructs include participants’ outcomes expectations (14 items), social support (7 items), self-regulation (6 items), and self-efficacy (5 items) constructs showed in Table 1.

Table 1 The constructs of SCT, number of items, range score, validity and reliability
Full size table

Validity and reliability

The content validity of measuring tools was established by the expert panel of ten academicians in this field (health education and health promotion experts and health care providers). The content validity ratio (CVR), content validity index (CVI), and reliability (Cronbach’s alpha for each item are presented in Table 1. The test–retest factor was 0.82 (2 weeks interval and among 25 participants).

Ethical considerations

The Ethical Committee of the Arak University of Medical Science, Iran, approved the study protocol (ID number- IR.ARAKMU.REC.1399.312). The purpose of the study was explained to the participants, and written consent was then obtained from participating mothers who volunteered to enter this study. Informed consent from illiterate participants were completed through face-to-face interviews at each health centre.

Statistical analysis

Data were analyzed through SPSS version 16 (SPSS, Inc., Chicago, IL, USA) statistical software using one-way ANOVA (with Bonferroni post hoc method), independent samples t-test, and multiple linear regressions. The significance level of α was considered 0.05 for all data. Data were analyzed by SPSS 16 using.

Results

The mean age was 37.8 years (SD = 12.2)and ranged from 17 to 81. Results showed that 52.6% (224/426) of the participants were female and 51.2% (281/426) had higher education. The mean (SD) of COVID-19 preventive behaviors was 43 (SD = 7.8) out of 60. About 241 participants (56.6%) had adequate self-regulation (95% Confidence Interval [CI] = 49.1%-70.6%), participants’ adequate self-efficacy was 60.3% (257/426) (95% CI = 39. 9% ‒79.6%), and 394 participants (92.5%) had intermediate outcomes expectations (95% CI = 63. 8% ‒98. 8%). Table 2 displays the association of SCT constructs related to COVID-19 and socio-demographic characteristics with the mean score of CPB. Economic status, education, and gender (p < 0.05) were statistically associated with CPB. Moreover according to the post hoc Bonferroni test, the average of preventive behaviors of Covid-19 is higher in people with over than 75 years old, people with higher socio-economic statuses and higher education, and people with higher self-regulation (p < 0.001).

Table 2 Distribution of socio-demographic characteristics, Mean and SD of CPB and their associated factors (n = 426)
Full size table

There was a statistical association between CPB and three constructs of SCT (outcomes expectations, self-regulation, and self-efficacy) (Table 3). Multiple regression showed that outcomes expectations (β = 0.11, p < 0.001), self-regulation (β = 0.41, p < 0.001), and self-efficacy (β = 0.30, p < 0.001), age, gender, and history of COVID 19infection were associated with CPB and those described 61% of the variance of CPB (Table 4). The distribution of each COVID-19 preventive behavior of the study participants is shown in Table 5 the rate of “Always” and “often” washing hands was 61.3% and 20.9% respectively.

Table 3 Correlation coefficients between each of the continuous variables
Full size table
Table 4 Regression analysis of predictor factors of behavior
Full size table
Table 5 Distribution of each COVID-19 preventive behaviors of the study participants
Full size table

Discussion

This study discovered the association between SCT constructs and COVID-19 preventive behaviors (CPB). To our knowledge, few documents have been published about the CPB via STC constructs.

In the present study, the self-regulatory (SR) structure influenced the participant’s CPB and the mean score of CPB among participants with adequate SR status was significantly higher than that of poor SR (47.2 vs 30.2). Lin et al.’s study [20], aimed to predict preventive behaviors of COVID-19 based on the SCT on 1718 participants, showed that action planning was one of the predictive structures of CPB. Also, this finding confirms the study of Sousa et al. [21] In this study it was also observed that the self-regulation structure directly affects health-related habits and mental health during the COVID-19 pandemic. Self-regulation is the process of setting goals and planning to perform the desired behavior by the individual [22] and the improvement of this structure in the individual leads to the facilitation of OVID-19 preventive behaviors. This structure can help a person to develop and implement his planned behavior and maintain it flexibly until he reaches his desired final goal [23, 24]. People with higher self-regulation maintain their behavior over time despite failures and adversities [25].

In a study Lauren et al. [26] about barriers to COVID-19 prevention behaviors among North Carolina residents showed that the most commonly endorsed barriers to 6-foot distancing included physical impediments, forgetting, and unfavorable descriptive norms.

Another result of this study was the positive effect of outcome expectations on adopting CPB. It can be said individuals who had an intermediate level of outcome expectations had a higher score CPB compared to people who had limited outcome expectations (43.7 vs. 34.3), which confirms the previous studies by Cavicchiolo et al. [27] and Sinicrope et al. [28].

In a study by Coroiu et al. [29] about barriers and facilitators of adherence to social distancing recommendations during COVID-19 among a large international sample of adults indicated that adherence to social distancing recommendations vary depending on the behaviour, with none of the surveyed behaviors showing perfect adherence. Strongest facilitators included wanting to protect the self, feeling a responsibility to protect the community, and being able to work/study remotely; strongest barriers included having friends or family who needed help with running errands and socializing in order to avoid feeling lonely.

A study by sinicrope et al. showed that the perceived outcomes expectations toward using a mask are effective on the behavior of using a mask [28]. Also, this finding is similar to a study that aimed to investigate behavioral intention to inject COVID-19 vaccination based on social cognitive theory, which showed that outcomes expectations of physical and self-evaluative outcomes were related to behavioral intention of COVID-19 vaccination among doctors and nurses [5].

In a study about COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries indicated that messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in community [30]. Hence, health promotion can highlight the benefits of COVID-19 prevention behaviors for both individuals and communities.

This study displays that COVID-19 prevention behaviors were significantly and positively associated with participants’ self-efficacy towards COVID-19 prevention behaviors (R = 0.55, P < 0.001). This result is reliable with previous studies that found a significant association between self-efficacy and COVID-19 prevention behaviors [29, 31]. In a study by Soltani et al. self-efficacy was significant a predictor of mask-wearing habits during the COVID-19 pandemic [15]. Barrett et al. also found self-efficacy associated with social distancing behavior during the COVID-19 pandemic among university students in the UK [31]. Self-efficacy is one of the key requirements to develop and keep health-related behavior [19]. Therefore, improving self-efficacy can lead to health habits and promotion of self-care behavior. Hence, based on improving self-efficacy through educational interventions and community massage could be useful.

Social support is the support that a person receives from others, especially family members and friends, to perform the desired behavior [32, 33] and is one of the key constructs of the SCT. In fact family is a crucial environmental factor influencing people’s health attitudes and behaviors.

Wanqi et al. [34] in a study about the role of family communication patterns in intergenerational COVID-19 discussions and preventive behaviors showed that the family communication patterns can be associated with preventive behaviors through different forms of family discussion about COVID-19. Conversation orientation is a strong facilitator for positive behavioral effects and scientific discussion is the most benign form of family health discussion. Health communication efforts should enhance the agency role of the family and motivate scientific discussion in health practices.

In the present study, there was a weak correlation between social support and CPB, which was not significant in the regression analysis. Kowalsky et al.’ study during the COVID-19 pandemic based on the social cognitive model, indicated a weak correlation was seen between subjective norms and behavior [33].

This study exposed that SCT theory was a significant predictor of the CPB and it described 61% of the variance. We found the clear effects of STC constructs (self-regulation, self-efficacy, and outcome expectancy) on participants’ COVID-19 preventive behaviors. These results are consistent with some studies. In the study by Raude et al. [18] which was conducted to investigate COVID-19 preventive behaviors based on cognitive, psychological, cultural, and social variables in France, cognitive variables predicted 23% of CPB. A study by Cavicchiolo et al., on 363 teenagers in 5 schools in Italy, demonstrated the structure SCT predicted 25% of the social distancing behavior during the COVID pandemic [27]. An online cross-section study by Barrett et al. shows that social cognitive model predicted 40% of hand washing and 25% of social distancing behavior [31]. In primary health care settings, this matter is vital for health services that design appropriate educational intervention programs based on SCT constructs about healthy behavior such as CPB.

In this study the almost participant use outdoor face mask, maintaining physical distance and avoid of crowded areas and social events. Also, this finding is similar to other studies [7, 10, 29].

There were some limitations in this study. First, the questionnaire was self-reported which could be subject to recall and response bias. Second, this study, might not lead to the casual interpretations of constructs of SCT and CPB. Despite limitations, the seemly, valid, and cost-effective method of data collection is self-assessment on adolescents and adults. This study also can create some other scientific evidence for future studies about health promotion communities.

Conclusion

Rendering to the result of this study constructs of SCT is the key predictor of participants’ CPB. Hence to design useful and effective behavioral change interventions based on evidence in the field of COVID-19 prevention. Finally based on these predictors, effective interventions and healthy messages could be designed based on this predictor outcomes expectations, self-regulation, social support, and self-efficacy which can be beneficial to improve healthy behavior.

Availability of the data and materials

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

SCT:

Social Cognitive Theory

CVR:

Content Validity Ratio

CVI:

Content Validity Index

SD:

Standard Deviation

References

  1. Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, et al. World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg. 2020;76:71–6.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Shigemura J, Ursano RJ, Morganstein JC, Kurosawa M, Benedek DM. Public responses to the novel 2019 coronavirus (2019-nCoV) in Japan: Mental health consequences and target populations. Psychiatry Clin Neurosci. 2020;74(4):281.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. World Health Organization COVID-19 weekly epidemiological update, 11 May 2023. 142nd ed. Geneva: World Health Organization; 2023. Available from: https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19-11-may-2023.

  4. Wang Y, Deng Z, Shi D. How effective is a mask in preventing COVID-19 infection?. Med Devices Sens. 2021;4(1):e10163. https://doi.org/10.1002/mds3.10163.

  5. She R, Chen X, Li L, Li L, Huang Z, Lau JTF. Factors associated with behavioral intention of free and self-paid severe acute respiratory coronavirus virus 2 (SARS-CoV-2) vaccination based on the social cognitive theory among nurses and doctors in China. Infect Control Hosp Epidemiol. 2022;43(5):609–15.

  6. Duan Y, Lippke S, Liang W, Shang B, Keller FM, Wagner P, et al. Association of social-cognitive factors with individual preventive behaviors of Covid-19 among a mixed-sample of older adults from China and Germany. Int J Environ Res Public Health. 2022;19(11):6364.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Taylor S, Asmundson GJG. Negative attitudes about facemasks during the COVID-19 pandemic: The dual importance of perceived ineffectiveness and psychological reactance. PloS One. 2021;16(2):e0246317. PubMed PMID: 33596207. Pubmed Central PMCID: PMC7888611. Epub 2021/02/18. eng.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. AlSaeed AA, Rabbani U. Explaining COVID-19 vaccine rejection using social cognitive theory in Qassim, Saudi Arabia. Vaccines (Basel). 2021;9(11):1304. https://doi.org/10.3390/vaccines9111304. PMID:34835235;PMCID:PMC8623049.

    Article  CAS  PubMed  Google Scholar 

  9. Larebo YM, Abame DE. Knowledge, attitudes, and practices of face mask utilization and associated factors in COVID-19 pandemic among Wachemo University Students, Southern Ethiopia: a cross-sectional study. PLoS One. 2021;16(9):e0257609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Honarvar B, Lankarani KB, Kharmandar A, Shaygani F, Zahedroozgar M, Rahmanian Haghighi MR, et al. Knowledge, attitudes, risk perceptions, and practices of adults toward COVID-19: a population and field-based study from Iran. Int J Public Health. 2020;65(6):731–9. PubMed PMID: 32583009. Pubmed Central PMCID: PMC7311321. Epub 2020/06/26. eng.

    Article  PubMed  Google Scholar 

  11. Hosseini F, et al. The impact of an educational intervention based on PRECEDE-PROCEED model on lifestyle changes among hypertension patients. Iran J Health Educ Health Promot. 2014;2(1):17–27.

    Google Scholar 

  12. Mirzaei-Alavijeh M, Hosseini S N, Niksirt M, Hashemian A H, Jalilian F. Determinants of Treatment Adherence Behaviors in Type 2 Diabetes Patients: An application of Social Cognitive Theory. ijdld 2023;23(1):1–12.

  13. Aschwanden D, Strickhouser JE, Sesker AA, Lee JH, Luchetti M, Terracciano A, et al. Preventive behaviors during the COVID-19 pandemic: associations with perceived behavioral control, attitudes, and subjective norm. Frontiers in Public Health. 2021;9:662835 PubMed PMID: PMC8139398. eng.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Moradi A, Soltani R, Shamsi M, Moradzadeh R. Effects of online social media on improving mothers’ behaviors towards preventing their children’s otitis media based on the PRECED model: a randomized educational intervention trial. BMC Pediatr. 2023;23(1):216. https://doi.org/10.1186/s12887-023-04016-y. PMID:37147633;PMCID:PMC10161150.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Soltani R, Shamsi M, Moradi A. Do perceived barriers, benefits, and severity have effect on mask-wearing habits during the coronavirus disease-2019 pandemic? Asian J Soc Health Behav. 2022;5:186–92. Available from: http://www.healthandbehavior.com/text.asp?2022/5/4/186/361712. Cited 2023 Jun 7.

    Article  Google Scholar 

  16. Glanz K, Rimer BK, Viswanath K. Theory, research, and practice in health behavior and health education. 4th ed. San Francisco: Wiley; 2008.

  17. Hagger MS, Hamilton K. Social cognition theories and behavior change in COVID-19: A conceptual review. Behav Res Ther. 2022;154:104095. https://doi.org/10.1016/j.brat.2022.104095. PMID: 35605335; PMCID: PMC9005242.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Raude J, Lecrique J-M, Lasbeur L, Leon C, Guignard R, Du Roscoät E, et al. Determinants of preventive behaviors in response to the COVID-19 pandemic in France: comparing the sociocultural, psychosocial, and social cognitive explanations. Front Psychol. 2020;11:584500.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bandura A. Social cognitive theory of selfregulation. Organ Behav Hum Decis Process. 1991;50(2):248–87.

    Article  Google Scholar 

  20. Lin CY, Imani V, Majd NR, Ghasemi Z, Griffiths MD, Hamilton K, et al. Using an integrated social cognition model to predict COVID-19 preventive behaviours. Br J Health Psychol. 2020;25(4):981–1005.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sousa SS, Ferreira MM, Cruz S, Sampaio A, Silva-Fernandes A. A structural equation model of self-regulation and healthy habits as an individual protective tool in the context of epidemics–evidence from COVID-19. Front Psychol. 2021:3819.

  22. Rovniak LS, Anderson ES, Winett RA, Stephens RS. Social cognitive determinants of physical activity in young adults: a prospective structural equation analysis. Ann Behav Med. 2002;24(2):149–56.

    Article  PubMed  Google Scholar 

  23. Kanfer FH. Self-regulation: research, issues, and speculations. Behav Modif Clin Psychol. 1970;74:178–220.

    Google Scholar 

  24. de Ridder DTD, de Wit JBF. Self-regulation in health behavior: concepts, theories, and central issues. Self‐regulation in health behavior; 2006. p. 1–23.

  25. Oettingen G, Bulgarella C, Henderson M, Gollwitzer PM. The self-regulation of goal pursuit. Motivational analyses of social behavior: building on Jack Brehm’s contributions to psychology. 2004. p. 225–44.

    Google Scholar 

  26. Lauren M, Hunter D, Maura D, Victoria S, Sophia M, Bartels B. Barriers to and facilitators of COVID-19 prevention behaviors among North Carolina residents. Health Educ Behav. 2022;49(2):231–41.

    Article  Google Scholar 

  27. Cavicchiolo E, Manganelli S, Girelli L, Cozzolino M, Lucidi F, Alivernini F. Adolescents at a distance: the importance of socio-cognitive factors in preventive behavior during the COVID-19 pandemic. Eur J Health Psychol. 2021;28(4):161.

    Article  Google Scholar 

  28. Sinicrope PS, Maciejko LA, Fox JM, Steffens MT, Decker PA, Wheeler P, et al. Factors associated with willingness to wear a mask to prevent the spread of COVID-19 in a Midwestern Community. Prev Med Rep. 2021;24:101543.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Coroiu A, Moran C, Campbell T, Geller AC. Barriers and facilitators of adherence to social distancing recommendations during COVID-19 among a large international sample of adults. PLoS One. 2020;15(10):e0239795. https://doi.org/10.1371/journal.pone.0239795.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Julio S, Solís A, Shana S, Niccolò F, Alexandra S, Nina M, et al. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat Med. 2021;27:1385–94.

    Article  Google Scholar 

  31. Barrett C, Cheung KL. Knowledge, socio-cognitive perceptions and the practice of hand hygiene and social distancing during the COVID-19 pandemic: a cross-sectional study of UK university students. BMC Public Health. 2021;21(1):1–18.

    Article  Google Scholar 

  32. Wolfe ME. An evaluation of an exercise adherence intervention using the social cognitive: the ohio State University. 2008.

    Google Scholar 

  33. Kowalsky JM, Mitchell AM, Okdie BM. Maintaining distance and avoiding going out during the COVID-19 pandemic: a longitudinal examination of an integrated social cognition model. Psychol Health. 2021:1–22.

  34. Wanqi G, Li C, Qin G, Fei S. The role of family communication patterns in intergenerational COVID-19 discussions and preventive behaviors: a social cognitive approach. BMC Psychology. 2023;11:290. https://doi.org/10.1186/s40359-023-01331-y.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Education Deputy of Arak University of Medical Sciences for their financial support, the participants for completing the questionnaires, and the healthcare staff for their cooperation in this study.

Funding

The present study is taken from a master's thesis approved by Arak University of Medical Sciences (Grant Number: 3801). The funder provided all costs of the thesis and did not have any role in the conducting and reporting of the study results.

Author information

Authors and Affiliations

  1. Department of Health Education and Health Promotion, School of Health, Arak University of Medical Sciences, Arak, Iran

    Raheleh Soltani, Mohsen Shamsi & Atefe Moradi

Authors
  1. Raheleh Soltani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohsen Shamsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atefe Moradi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RS, conceptualized and designed the study, collected the data, drafted and revised the manuscript; MS and AM, conceptualized and designed the study, collected the data, initially analyzed and interpreted the data; drafted and revised the manuscript; and all authors approved the final manuscript as submitted, and agreed to be accountable for all aspects of the work in ensuring that the questions related to the accuracy or integrity of any parts of the work will be appropriately investigated and resolved.

Corresponding author

Correspondence to Mohsen Shamsi.

Ethics declarations

Ethics approval and consent to participate

The present study was approved by the Research Council of Arak University of Medical Sciences (Grant Number: 3801). The ethics committee approval code number is (IR.ARAKMU.REC.1399.312). Informed written consent was obtained from the participant and all the procedures performed in the study involving human participants were in accordance with the ethical standards. Informed consent from illiterate participants was completed through face-to-face interviews at each health centres.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soltani, R., Shamsi, M. & Moradi, A. Determine the factors that affected COVID-19 prevention behaviors based on constructs of social cognition theory. BMC Public Health 23, 2312 (2023). https://doi.org/10.1186/s12889-023-17209-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-17209-y

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us