How Trust in Information Sources Influences Preventative Measures Compliance during the COVID-19 Pandemic
Abstract
:1. Introduction
2. Theoretical Frameworks
3. Materials and Methods
3.1. Data Collection Procedures
3.2. Participants
3.3. Instruments
3.4. Statistical Analysis
4. Results
4.1. Hypothesized Model
4.2. Gender Differences
4.3. Cross-Cultural Differences
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Timeline of WHO’s Response to COVID-19. World Health Organization. Available online: https://www.who.int/news-room/detail/29-06-2020-covidtimeline (accessed on 30 July 2020).
- United States Department of Defense. Coronavirus: DOD Response Timeline. 2020. Available online: https://www.defense.gov/Explore/Spotlight/Coronavirus/DOD-Response-Timeline/ (accessed on 30 July 2020).
- Burris, S.; de Guia, S.; Gable, L.; Levin, D.; Parmet, W.E.; Terry, N.P. The legal response to COVID-19: Legal pathways to a more effective and equitable response. J. Public Health Manag. Pr. 2021, 27 (Suppl. 1), S72–S79. [Google Scholar] [CrossRef]
- de Zwart, O.; Veldhuijzen, I.K.; Elam, G.; Aro, A.R.; Abraham, T.; Bishop, G.D.; Voeten, H.A.C.M.; Richardus, J.H.; Brug, J. Perceived threat, risk perception, and efficacy beliefs related to SARS and other (emerging) infectious diseases: Results of an international survey. Int. J. Behav. Med. 2009, 16, 30–40. [Google Scholar] [CrossRef] [Green Version]
- Leppin, A.; Aro, A.R. Risk perceptions related to SARS and Avian Influenza: Theoretical foundations of current empirical research. Int. J. Behavioral. Med. 2009, 16, 7–29. [Google Scholar] [CrossRef] [Green Version]
- Liao, Q.; Cowling, B.; Lam, W.; Ng, M.; Fielding, R. Situational awareness and health protective responses to pandemic influenza A (H1N1) in Hong Kong: A cross-sectional study. PLoS ONE 2010, 5, e13350. [Google Scholar] [CrossRef] [Green Version]
- Tulchinsky, T.H. John Snow, Cholera, the broad street pump; waterborne diseases then and now. Case Stud. Public Health 2018, 77–99. [Google Scholar] [CrossRef]
- Centers for Disease Control. Crisis and Risk Communication: Introduction. 2018. Available online: https://emergency.cdc.gov/cerc/ppt/CERC_Introduction.pdf (accessed on 31 July 2020).
- Henrich, N.; Holmes, B. Communicating during a pandemic: Information the public wants about the disease and new vaccines and drugs. Health Promot. Pract. 2011, 12, 610–619. [Google Scholar] [CrossRef] [PubMed]
- Bradley, D.T.; Mansouri, M.A.; Kee, F.; Garcia, L.M.T. A systems approach to preventing and responding to COVID-19. Eclinicalmedicine 2020, 21, 100325. [Google Scholar] [CrossRef] [Green Version]
- Adolph, C.; Amano, K.; Bang-Jensen, B.; Fullman, N.; Wilkerson, J. Pandemic politics: Timing state-level social distancing responses to COVID-19. J. Health Politics Policy Law 2020, 46, 211–233. [Google Scholar] [CrossRef] [PubMed]
- Gigliotti, P.; Martin, E.G. Predictors of state-level stay-at-home orders in the United States and their association with mobility of residents. J. Public Health Manag. Pract. 2020, 26, 622–631. [Google Scholar] [CrossRef] [PubMed]
- Ali, S.H.; Foreman, J.; Tozan, Y.; Capasso, A.; Jones, A.M.; DiClemente, R.J. Trends and predictors of COVID-19 information sources and their relationship with knowledge and beliefs related to the pandemic: Nationwide cross-sectional study. JMIR Public Health Surveill. 2020, 6, e21071. [Google Scholar] [CrossRef]
- Fridman, I.; Lucas, N.; Henke, D.; Zigler, C.K. Association between public knowledge about COVID-19, trust in information sources, and adherence to social distancing: Cross-sectional survey. JMIR Public Health Surveill. 2020, 6, e22060. [Google Scholar] [CrossRef]
- Verma, A.; Gunjawate, D.R.; Bhushan Kumar, S.; Bharath, C.S.; Ravi, R. COVID-19–what do we know and how are we dealing with it? A quick online cross-sectional study in India. J. Health Res. 2020. [Google Scholar] [CrossRef]
- Carpenter, C.J. A meta-analysis of the effectiveness of Health Belief Model variables in predicting behavior. Health Commun. 2010, 25, 661–669. [Google Scholar] [CrossRef] [Green Version]
- Rosenstock, I.M.; Strecher, V.J.; Becker, M.H. Social learning theory and the Health Belief Model. Health Educ. Q. 1988, 15, 175–183. [Google Scholar] [CrossRef] [PubMed]
- Callow, M.; Callow, D.; Smith, C. Older adults’ intention to socially isolate once COVID-19 stay-at-home orders are replaced with “safer-at-home” public health advisories: A survey of respondents in Maryland. J. Appl. Gerontol. 2020. [Google Scholar] [CrossRef]
- Strecher, V.J.; McEvoy DeVellis, B.; Becker, M.H.; Rosenstock, I.M. The role of self-efficacy in achieving health behavior change. Health Educ. Q. 1986, 13, 73–92. [Google Scholar] [CrossRef] [Green Version]
- Ajzen, I.A. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J. Appl. Soc. Psychol. 2002, 32, 665–683. [Google Scholar] [CrossRef]
- Qazi, A.; Qazi, J.; Naseer, K.; Zeeshan, M.; Hardaker, G.; Maitama, J.Z.; Haruna, K. Analyzing situational awareness through public opinion to predict adoption of social distancing amid pandemic COVID-19. J. Med. Virol. 2020, 92, 849–855. [Google Scholar] [CrossRef]
- Wheeler, A.R.; Shanine, K.K.; Leon, M.R.; Whitman, M.V. Student recruited samples in organizational research: A review, analysis, and guidelines for future research. J. Occup. Organ. Psychol. 2014, 87, 1–26. [Google Scholar] [CrossRef]
- Perry, R.H.; Charlotte, B.; Isabella, M.; Bob, C. SPSS Explained; Taylor & Francis: Abingdon, UK, 2004. [Google Scholar]
- Costa, P.T.; Terracciano, A.; McCrae, R.R. Gender differences in personality traits across cultures: Robust and surprising findings. J. Pers. Soc. Psychol. 2001, 81, 322–331. [Google Scholar] [CrossRef] [PubMed]
- Baldassare, M.; Feller, S. Cultural variations in personal space: Theory, methods, and evidence. Ethos 1975, 3, 481–503. [Google Scholar] [CrossRef]
- Zhuo-ying, K.E.; Yu, L.I.A.N. An analysis on proxemics phenomenon between China and America. J. Lit. Art Stud. 2017, 7, 1320–1325. [Google Scholar] [CrossRef] [Green Version]
M | STD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
---|---|---|---|---|---|---|---|---|---|---|
1. Formal | 8.72 | 2.51 | (0.62) | |||||||
2. Informal | 4.37 | 1.67 | 0.08 | (0.50) | ||||||
3. Understand | 4.41 | 0.80 | 0.14 ** | 0.08 | ||||||
4. Efficacy | 3.70 | 1.07 | 0.04 | 0.13 ** | 0.27 *** | |||||
5. Susceptible | 7.12 | 1.28 | −0.00 | 0.03 | 0.04 | 0.37 *** | (0.75) | |||
6. Worry | 3.48 | 1.07 | 0.24 *** | −0.01 | 0.03 | −0.15 *** | −0.30 *** | |||
7. Preventive | 19.00 | 2.85 | 0.14 ** | −0.02 | 0.08 | 0.10 * | −0.02 | 0.26 *** | (0.66) | |
8. Distancing | 18.89 | 2.32 | −0.19 *** | −0.00 | −0.10 * | −0.02 | 0.01 | −0.28 *** | −0.42 *** | (0.51) |
Path | USA | Hong Kong |
---|---|---|
Trust Formal→Understanding | 0.15 a | 0.36 a |
Trust Formal→Self-Efficacy | 0.25 a | |
Trust Formal→Susceptibility | ||
Trust Formal→Worry | 0.24 a | |
Trust Informal→Understanding | ||
Trust Informal→Self-Efficacy | 0.14 a | |
Trust Informal→Susceptibility | −0.21 a | |
Trust Informal→Worry | 0.16 b | |
Understanding→Self-Efficacy | 0.27 a | |
Susceptibility→Self-Efficacy | −0.36 a | −0.42 a |
Susceptibility→Worry | 0.30 a | 0.44 a |
Understanding→Preventative | 0.19 a | |
Understanding→Distancing | ||
Self-Efficacy→Preventative | 0.13 b | 0.23 a |
Self-Efficacy→Distancing | 0.13c | |
Susceptibility→Preventative | ||
Susceptibility→Distancing | ||
Worry→Preventative | 0.29 a | 0.13 c |
Worry→Distancing | 0.28 a | 0.36 a |
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Maykrantz, S.A.; Gong, T.; Petrolino, A.V.; Nobiling, B.D.; Houghton, J.D. How Trust in Information Sources Influences Preventative Measures Compliance during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 5867. https://doi.org/10.3390/ijerph18115867
Maykrantz SA, Gong T, Petrolino AV, Nobiling BD, Houghton JD. How Trust in Information Sources Influences Preventative Measures Compliance during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2021; 18(11):5867. https://doi.org/10.3390/ijerph18115867
Chicago/Turabian StyleMaykrantz, Sherry A., Tao Gong, Ashley V. Petrolino, Brandye D. Nobiling, and Jeffery D. Houghton. 2021. "How Trust in Information Sources Influences Preventative Measures Compliance during the COVID-19 Pandemic" International Journal of Environmental Research and Public Health 18, no. 11: 5867. https://doi.org/10.3390/ijerph18115867