Volume 24, Issue 1 (3-2021)
jha 2021, 24(1): 54-67 |
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latifi M, Davaridolatabadi N, Shahi M. The Effect of Virtual Social Networks on Users' Self-Care of Covid-19: A Structural Equation Modeling. jha 2021; 24 (1) :54-67
URL: http://jha.iums.ac.ir/article-1-3447-en.html
URL: http://jha.iums.ac.ir/article-1-3447-en.html
1- PhD in Information Sciences and Knowledge, Social Determinants in Health Promotion Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
2- Associate Professor, School of Allied Medical Sciences, Hormozgan University of Medical Sciences, Bandar Abbas, Iran. ,. davarin@gmail.com
3- Associate Professor, School of Allied Medical Sciences, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
2- Associate Professor, School of Allied Medical Sciences, Hormozgan University of Medical Sciences, Bandar Abbas, Iran. ,
3- Associate Professor, School of Allied Medical Sciences, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Abstract: (3212 Views)
Introduction: Today, social networks provide a good opportunity for self-care. The aim of this study was to identify the impact and relationship of virtual social networks on users' self-care of COVID-19 and to achieve a structural equation modeling.
Methods: In the present descriptive-analytical study, the measuring instrument was an online questionnaire extracted from Orem’s self-care model(2011) adapted based on the objectives of study. The study sample included 662 social network users (WhatsApp, Instagram, and Telegram users) in Hormozgan province; they were selected through convenience sampling. Modeling was carried out using SPSS and Amos software.
Results: The results of the present research indicated a significant and direct relationship between the independent variables of “presence and interaction in the social networks” and “users’ orientation to the type of social network” with the dependent variable of “self-care towards COVID-19” (p< 0.0009). In addition, 45 percent of changes in self-care variable was covered by a set of social networking indices. The structural equation Modelling (SEM) showed that in the self-care variable, the dimension of psychological support with a standard coefficient of 0.99 had the highest effect, and the dimension of awareness and attention to the effects and results of COVID-19 with a standard coefficient of 0.95 had the lowest effect with regard to the variables of presence and interaction in the network and users’ orientation to the type of network.
Conclusion: Since social networks have been able to affect users' self-care, health officials and disease control and prevention centers can use the potential of social networks in self-care.
Methods: In the present descriptive-analytical study, the measuring instrument was an online questionnaire extracted from Orem’s self-care model(2011) adapted based on the objectives of study. The study sample included 662 social network users (WhatsApp, Instagram, and Telegram users) in Hormozgan province; they were selected through convenience sampling. Modeling was carried out using SPSS and Amos software.
Results: The results of the present research indicated a significant and direct relationship between the independent variables of “presence and interaction in the social networks” and “users’ orientation to the type of social network” with the dependent variable of “self-care towards COVID-19” (p< 0.0009). In addition, 45 percent of changes in self-care variable was covered by a set of social networking indices. The structural equation Modelling (SEM) showed that in the self-care variable, the dimension of psychological support with a standard coefficient of 0.99 had the highest effect, and the dimension of awareness and attention to the effects and results of COVID-19 with a standard coefficient of 0.95 had the lowest effect with regard to the variables of presence and interaction in the network and users’ orientation to the type of network.
Conclusion: Since social networks have been able to affect users' self-care, health officials and disease control and prevention centers can use the potential of social networks in self-care.
Type of Study: Research |
Subject:
Health Information Management
Received: 2021/01/18 | Accepted: 2021/03/17 | Published: 2021/05/9
Received: 2021/01/18 | Accepted: 2021/03/17 | Published: 2021/05/9
References
1. Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. Int J Infect Dis . 2020;92:214-7. [DOI:10.1016/j.ijid.2020.01.050]
2. Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: A mathematical modelling study. Lancet Infect Dis. 2020;20(5): 553-58. [DOI:10.1016/S1473-3099(20)30144-4]
3. World Health Organization. Mental health and psychosocial considerations during the COVID-19 outbreak, 18 March 2020. [Pamphlet]. Geneva: World Health Organization; 2020.
4. Denyes MJ, Orem DE, Bekel G, SozWiss. Self-care: A foundational science. Nurs Sci Q. 2001;14(1):48-54. [DOI:10.1177/089431840101400113]
5. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. The Lancet. 2020;395(10223):507-13. [DOI:10.1016/S0140-6736(20)30211-7]
6. Ji Y, Ma Z, Peppelenbosch MP, Pan Q. Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob Health. 2020;8(4):e480. [DOI:10.1016/S2214-109X(20)30068-1]
7. Hessen MT. Novel Coronavirus Information Center: Expert guidance and commentary [Internet]. 2020 [Updated 2021 Apr 2;cited 2020 Jan 8] .Available from: https://www.elsevier.com/connect/coronavirus-information-center.
8. Zigron S, Bronstein J. "Help is where you find it": The role of weak ties networks as sources of information and support in virtual health communities. J Assoc Inf Sci Technol. 2019;70(2):130-9. [DOI:10.1002/asi.24106]
9. Lin H-Ch, Chang Ch-M. What motivates health information exchange in social media? The roles of the social cognitive theory and perceived interactivity. Inform Manag. 2018;55(6):771-80. [DOI:10.1016/j.im.2018.03.006]
10. Lau AY, Gabarron E, Fernandez-Luque L, Armayones M. Social media in health-what are the safety concerns for health consumers? Health Inf Manag. 2012;41(2):30-5. [DOI:10.1177/183335831204100204]
11. Abd-Alrazaq A, Alhuwail D, Househ M, Hamdi M, Shah Z. Top concerns of tweeters during the COVID-19 pandemic: infoveillance study. J Med Internet Res. 2020;22(4):e19016. [DOI:10.2196/19016]
12. González-Padilla DA, Tortolero-Blanco L. Social media influence in the COVID-19 pandemic.Int Braz J Urol. 2020;46 (suppl.1):120-24. [DOI:10.1590/s1677-5538.ibju.2020.s121]
13. Yu S-Ch, Chen H-R, Liu A-Ch, Lee H-Y. Toward COVID-19 Information: Infodemic or Fear of Missing Out? Healthcare (Basel). 2020; 8(4): 550. [DOI:10.3390/healthcare8040550]
14. Niknam F, Samadbeik M, Fatehi F, Shirdel M, Rezazadeh M, Bastani P. COVID-19 on instagram: A content analysis of selected accounts. Health Policy Technol. 2021;10(1):165-73. [DOI:10.1016/j.hlpt.2020.10.016]
15. Shoaei MD, Dastani M. The Role of Twitter During the COVID-19 Crisis: A Systematic Literature Review. Acta Informatica Pragensia.2020; 9(2):154-69. [DOI:10.18267/j.aip.138]
16. Richardson ChE. Coping with the strange world of COVID-19 .Community connections Newspaper.2020 Sep 18; Sect:MED. 1-16 (col. 3).
17. Reddy BV, Gupta A. Importance of effective communication during COVID-19 infodemic. J Family Med Prim Care. 2020;9(8):3793- 796. [DOI:10.4103/jfmpc.jfmpc_719_20]
18. Zuo Y, Ma Y, Zhang M, Wu X, Ren Zh. The impact of sharing physical activity experience on social network sites on residents' social connectedness: A cross-sectional survey during COVID-19 social quarantine. Global Health. 2021;17(1):1-12. [DOI:10.1186/s12992-021-00661-z]
19. Cuello-Garcia C, Pérez-Gaxiola G, van Amelsvoort L. Social media can have an impact on how we manage and investigate the COVID-19 pandemic. J Clin Epidemiol. 2020;127:198-201. [DOI:10.1016/j.jclinepi.2020.06.028]
20. Chan AKM, Nickson CP, Rudolph JW, Lee A, Joynt GM. Social media for rapid knowledge dissemination: early experience from the COVID‐19 pandemic. Anaesthetists; 2020; 75(12): 1579-582. [DOI:10.1111/anae.15057]
21. Ruddock A. Digital media influence: A cultivation approach. London: SAGE; 2020.
22. Kaplan AM, Haenlein M. Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz. 2010;53(1):59-68. [DOI:10.1016/j.bushor.2009.09.003]
23. Orem DE, Taylor SG. Reflections on nursing practice science: the nature, the structure, and the foundation of nursing sciences. Nurs Sci Q. 2011;24(1):35-41. [DOI:10.1177/0894318410389061]
24. Yang F, Wei X. Application of Orem Self-care Model in Early Rehabilitation Nursing of Patients with Cerebral Infarction. Invest Clin. 2020;61(3):1447-456.
25. World Health Organization. Coronavirus disease 2019 (COVID-19): situation report -72. Geneva: World Health Organization(WHO); 2020 Apr 11. 1-13 p. Report No:72.
26. Shojaei P, Bordbar N, Ghanbarzadegan A, Najibi M, Bastani P. Ranking of Iranian provinces based on healthcare infrastructures: before and after implementation of Health Transformation Plan. Cost Eff Resour Alloc. 2020;18(4):1-13. [DOI:10.1186/s12962-020-0204-5]
27. Top Sites in Iran amazon. Alexa [Internet]. 2020 [Updated 2021 Apr 1;cited 2020 Apr 8]. Available from: https://www.alexa.com/topsites/countries/IR.
28. Apuke OD, Omar B. User motivation in fake news sharing during the COVID-19 pandemic: an application of the uses and gratification theory. Online Information Review. 2020; 45(1):220-39. [DOI:10.1108/OIR-03-2020-0116]
29. Fernandez-Robin C, Yanez D, McCoy S. Intention to use whatsApp, Artificial Intelligence - Scope and Limitations. In: Harkut DG, Editor. Artificial Intelligence: Scope and Limitations London: IntechOpen; 2019. p.89-101. [DOI:10.5772/intechopen.81999]
30. Kamel Boulos MN, Giustini DM, Wheeler S. Instagram and whatsApp in health and healthcare: An overview. Future Internet. 2016;8(3):37. [DOI:10.3390/fi8030037]
31. Kocak E, Nasir VA, Turker HB. What drives Instagram usage? User motives and personality traits. Online Information Review. 2020;44(3): 625-43. [DOI:10.1108/OIR-08-2019-0260]
32. Ayre C, Scally AJ. Critical values for Lawshe's content validity ratio: Revisiting the original methods of calculation. Meas Eval Couns Dev. 2014;47(1):79-86. [DOI:10.1177/0748175613513808]
33. Ullman JB, Bentler PM. Structural equation modeling. In: Freedheim, DK, editor.Handbook of psychology. vol 1. New York: Wily; 2003. p.607-34.
34. Sap S, Kondo E, Sobngwi E, Mbono R, Tatah S, Dehayem M, et al. Effect of patient education through a social network in young patients with type 1 diabetes in a Sub‐Saharan context. Pediatr Diabetes. 2019;20(3):361-65. [DOI:10.1111/pedi.12835]
35. Omidi Z, Kheirkhah M, Abolghasemi J, Haghighat S. Effect of lymphedema self-management group-based education compared with social network-based education on quality of life and fear of cancer recurrence in women with breast cancer: a randomized controlled clinical trial. Qual Life Res. 2020; 29(7):1789-1800. [DOI:10.1007/s11136-020-02455-z]
36. Mohammadzadeh Z, Davoodi S, Ghazisaeidi M. Online social networks-opportunities for empowering cancer patients. Asian Pac J Cancer Prev. 2016;17(3):933-36. [DOI:10.7314/APJCP.2016.17.3.933]
37. Lee S, Vishwanath A. Examining Predictors of Self-Care Behaviors in Korean Patients with Chronic Hepatitis B. Curr Psychol. 2019;38(3):890-900. [DOI:10.1007/s12144-017-9664-y]
38. Ukoha Ch. On the Value of Healthcare Social Media: Exploring Users' Perspectives. In: proceedings of the 22nd Pacific Asia Conference on Information Systems (PACIS); 2018 Sep 27-30; Yokohama, Japan: Association for Information Systems; 2018. p. 205-15.
39. Lin Ch-Y, Broström A, Griffiths MD, Pakpour AH. Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia. Internet Interv. 2020;21:100345. [DOI:10.1016/j.invent.2020.100345]
40. Islam MS, Sarkar T, Khan SH, Kamal A-HM, Hasan SM, Kabir A, et al. COVID-19-related infodemic and its impact on public health: A global social media analysis. Am J Trop Med Hyg. 2020;103(4):1621-629. [DOI:10.4269/ajtmh.20-0812]
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