Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Exploring What Effects on Vaccination for Covid-19: Converging Health Locus of Control and Health Belief Model

코로나 19 백신 접종영향 요인의 탐색: 건강통제소재와 건강신념모형의 융합

  • Joo, Jihyuk (College of General Education, Far East University)
  • Received : 2021.08.11
  • Accepted : 2021.11.20
  • Published : 2021.11.28
Download PDF
⟨ Previous Next ⟩

Abstract

Since the outbreak of Covid-19, many countries have tried to defense Covid-19 to protect their people and as an influential and reliable policy as of now, they have recommended vaccinating. Thus, this research explored what influences the intention to vaccinate against Covid-19 with three health locus of control from multi-dimension health locus of control (MHLC) and perceived susceptibility and severity from health belief model (HBM) through PLS path modeling. Consequently, chance locus of control (CHLC) influence indirectly intention to vaccinate against Covid-19 mediating with susceptibility perception. It implies that the more fatalistic people attitude toward Covid-19, the more susceptible they perceived to the disease, and then, the stronger intention to vaccinate they would have. Thus, the health promotion authorities should motivate to activate people's susceptibility perception toward the disease through utilizing a variety of policies and consider that the fatalistic tendency toward the disease of people could play an antecedent role in the process.

코로나 19가 발생한 이래 각국은 국민들을 코로나19로부터 보호하기 위해서 다양한 정책을 펴고 있고, 가장 유력한 방안으로 백신접종을 권장하고 있다. 이에 본 연구는 대학생들의 대상으로 백신접종에 어떤 요인들이 영향을 미치는지를 규명하기 위해 다차원건강통제소재의 3가지 건강통제소재와 건강신념모형의 지각된 취약성과 심각성을 융합하여 백신접종 의도를 탐색하였다. PLS경로모형 분석을 실시한 결과 최종적으로 우연 건강통제소재(CHLC)가 취약성 지각을 매개하여 백신접종의도에 영향을 미치고 있었다. 이는 코로나 19에 대해 운명론적 태도가 클수록 코로나 19에 대해 더 취약하다고 인식하고 백신을 접종하려는 의도가 더 커진다는 것을 의미한다. 따라서 예방접종율을 높이기 위해서는 보건당국은 다양한 방안을 활용하여 국민들의 질병에 대한 감수성 인식을 활성화 하도록 동기를 부여할 필요가 있다. 이 과정에서 사람들의 숙명론적 경향이 선행변인 역할을 할 수 있음도 고려해야 한다.

Keywords

References

  1. H. S. Lim. (2005). Changing patterns of communicable diseases in Korea. Journal of Preventice Medicine and Public Health, 38(2), 117-124.
  2. D. S. Heo. (2015. June 8). Lessons from swine flu in 2009 and 'MERS' . http://www.medicaltimes.com/News/1097552
  3. J. Cho & I. Cho. (2019). Exploration of online issues about re-influx of MERS virus in Korea 2018: Investigation through topic modeling analysis & emotion analysis. Journal of Digital Contents Society, 20(5), 1051-1060. https://doi.org/10.9728/dcs.2019.20.5.1051
  4. M. K. Kim & E. J. Lee. (2016). The study on behavior intention of health protection effected by news media and news usage, news reliability on MERS outbreak. Broadcasting & Arts Research Institute, 11(1), 119-144.
  5. J. H. Nam. (2015). Record of MERS for 70 days. Kwanhun Journal, 136, 16-22.
  6. J. Moon & S. L. Han. (2016). Analysis of the influence of MERS epidemic on retailing industry of Seoul city. Journal of Channel & Retailing, 21(1), 129-152.
  7. H. K. Park, S. Kim & J. A. Yang. (2016). The effects of exposure to MERS information and issue involvement on perceived information influence, prevention and information sharing. Journal of Media Economics and Culture, 14(3), 7-48.
  8. J. H. Hong & M. N. Lee. (2017). The influence of social media's environmental characteristics on users' active participation and the types of message diffusion: Government's communication message and public responses during the MERS outbreak. Korean Society of Internet Information, 18(1), 89-103.
  9. H. Jeon. (2016). An analysis of risk communication: A case study of MERS-CoV in Korea. Crisisonomy, 12(5), 143-155.
  10. S. S. Hwang. (2015). MERS statistics. Hwanghae Review, 88, 224-236.
  11. D. H. Kim. (2015). The policy for non-disclosure of information should have been questioned. Kwanhun Journal, 136, 23-28.
  12. Y. Kim. (2016). An essay on Korean media's coverage of Middle East Respiratory. Korean Academy on Communication in Healthcare, 11(1), 39-50. https://doi.org/10.15715/kjhcom.2016.11.1.39
  13. G. O. Lee. (2015). The press must play the role of both information-provider and watchdog. Kwanhun Journal, 136, 29-38.
  14. B. S. Wallston, & K. A. Wallston. (1978). Locus of control and health: A review of the Literature. Health Education Monographs, 6(2), 107-117. https://doi.org/10.1177/109019817800600102
  15. (October 25.2013). Locus of control. The Glossary of Education Reform. https://www.edglossary.org/locus-of-control/
  16. B. S. Wallston, K. A. Wallston, G. D. Kaplan, & S. A. Maides. (1976). Development and validation of the health locus of control (HLC) scale. Journal of consulting and clinical psychology, 44(4), 580-585. https://doi.org/10.1037/0022-006X.44.4.580
  17. A. P. Kassianos, M. Symeou, & M. Ioannou. (2016). The health locus of control concept: Factorial structure, psychometric properties and form equivalence of the Multidimensional Health Locus of Control scales. Health Psychology Open, 3(2), 1-10. DOI : 10.1177/2055102916676211
  18. K. A. Wallston, B. Strudler Wallston, & R. DeVellis. (1978). Development of the multidimensional health locus of control (MHLC) scales. Health education monographs, 6(1), 160-170. https://doi.org/10.1177/109019817800600107
  19. J. B. Rotter. (1954). Social Learning and Clinical Psychology. Englewood Cliffs, NJ: Prentice-Hall, Inc.
  20. H. Levenson. (1973). Multidimensional locus of control in psychiatric patients. Journal of consulting and clinical psychology, 41(3), 397-404. https://doi.org/10.1037/h0035357
  21. H. Levenson. (1974). Activism and powerful others: Distinctions within the concept of internal-external control. Journal of personality assessment, 38(4), 377-383. https://doi.org/10.1080/00223891.1974.10119988
  22. T. Sorlie, & H. C. Sexton. (2001). Predictors of the process of coping in surgical patients. Personality and Individual Differences, 30(6), 947-960. https://doi.org/10.1016/S0191-8869(00)00085-4
  23. S. Bergvik, T. Sorlie & R. Wynn. (2012). Coronary patients who returned to work had stronger internal locus of control beliefs than those who did not return to work. British Journal of Health Psychology, 17(3), 596-608. https://doi.org/10.1111/j.2044-8287.2011.02058.x
  24. E. J. Burker, D. M. Evon, J. Galanko & T. Egan. (2005). Health locus of control predicts survival after lung transplant. Journal of health psychology, 10(5), 695-704. https://doi.org/10.1177/1359105305055326
  25. A. M. Brincks, D. J. Feaster, M. J. Burns & V. B. Mitrani. (2010). The influence of health locus of control on the patient-provider relationship. Psychology, Health & Medicine, 15(6), 720-728. https://doi.org/10.1080/13548506.2010.498921
  26. S. Bergvik, T. Sorlie & R. Wynn. (2010). Approach and avoidance coping and regulatory focus in patients having coronary artery bypass graft surgery. Journal of Health Psychology, 15(6), 915-924. https://doi.org/10.1177/1359105309359542
  27. E. Maunsell, M. Drolet, J. Brisson, J. Robert & L. Deschenes. (2002). Dietary change after breast cancer: extent, predictors, and relation with psychological distress. Journal of Clinical Oncology, 20(4), 1017-1025. https://doi.org/10.1200/JCO.20.4.1017
  28. J. Joo. (2017). Exploration of Structural Relations on Health Behavior Related to Particulate Matter: Focused on Multi-Dimensional Health Locus of Control, Perceived Susceptibility and Severity, and Health Behavioral Intention. Journal of the Korea Convergence Society, 8(1), 413-421.
  29. A. Baban & C. Craciun. (2007). Changing health-risk behaviors: A review of theory and evicdence-based intervention in health psychology. Journal of Cognitive and Behavioral Psychotherapies, 7(1), 45-67.
  30. P. Shreen & C. Abraham. (1996). The health belief model. In M. Conner & P. Norman (Eds.), Predicting Health Behvior. Buckingham: Open University Press.
  31. I. M. Rosenstock. (1974). The health beief model and preventive health behavior. Helath Education Monographs, 2, 93-114.
  32. J. Joo. (2018). A study on Korean collegians' health perception toward eggs contaminated with pesticide: Will preventive behavioral intention be predicted by perceived susceptibility and severity, trust in government, evaluation of information from government, and subjective knowledge? Journal of Korean Convergence Society, 9(12), 355-363.
  33. Y. Kim, H. Lee, Y. Jang & H. Lee. (2015). How does media construct particulate matter risks? A new frame and source analysis on prticulate matter. Korean Journal of Journalism & Communication Studies, 59(2), 121-154.
  34. C. M. Ringle, S. Wende, & J. M. Becker. (2015). SmartPLS 3. www.smartpls.com
  35. D. Gefen & D. W. Straub. (2004). Consumer trust in B2C e-commerce and the importnace of social presence: Experiments in e-products and e-services Omega, 32(6), 407-424. https://doi.org/10.1016/j.omega.2004.01.006
  36. J. Joo. (2013). Exploring relationships among Korean children's depression, smartphone addiction, and school life satisfaction: Focusing on partial least square (PLS) path modeling. Journal of Digital Policy & Management, 11(12), 49-60.
  37. M. Hajli. (2012). An integrated model for e-commerce adoption at the customer level with the impact of social commerce. International Journal of Information Science and Management, (Special Issue), 77-97.
  38. D. Gefen, D. W. Straub & M. Boudreau. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4, 2-77.
  39. W. W. Chin. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii-xvi.
  40. P. Chwelos, I. Benbasat, & A. S. Dexter. (2001). Empirical test of an EDI adoption model. Information Systems Research, 12(3), 304-321. https://doi.org/10.1287/isre.12.3.304.9708
  41. J. Cohen. (1988), Statistical Power Analysis for the Behavioral Sciences(2nd Ed.), Lawrence Erlbaum Associates.