Palabras clave
Sars-CoV-2
infecciones por coronavirus
actitudes
conducta social
teoría del comportamiento planificado
Cómo citar
Resumen
Se ha demostrado que la teoría del comportamiento planificado (TCP) es un predictor eficiente de los comportamientos relacionados con la salud. Esta teoría propone que tres variables psicológicas predicen la intención de comportamiento: actitud, normas subjetivas, percepción de control. La intención conductual explica el comportamiento en sí. Este estudio tuvo como objetivo probar el poder predictivo del TCP en el aislamiento social del Sars-CoV-2. Participaron 1.139 adultos, con edad promedio de 35.5 años, de todas las regiones de Brasil. Los resultados mostraron índices de ajuste adecuados de los modelos predictivos de TCP sobre aislamiento social. TCP explicó 30.7% de la variación del nivel de aislamiento percibido y 11.5% de la variación del número de veces que salió de casa. Entre los componentes del TCP, la actitud demostró ser el factor con mayor poder predictivo sobre las variables de aislamiento social. Los resultados obtenidos pueden apoyar campañas de prevención basadas en los cambios de actitudes.
Citas
Abraham, C., Clift, S., & Grabowski, P. (1999). Cognitive predictors of adherence to malaria prophylaxis regimens on return from a malarious region: A prospective study. Social Science & Medicine, 48(11), 1641-1654. doi:https://doi.org/10.1016/S0277-9536(98)00455-9
Agarwal, V. (2014). A/H1N1 vaccine intentions in college students: An application of the theory of planned behavior. Journal of American College Health, 62(6), 416-424. doi:https://doi.org/10.1080/07448481.2014.917650
Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago: Dorsey Press.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi:https://doi.org/10.1016/07495978(91)90020-t
Ajzen, I., & Cote, N.G. (2008). Attitudes and the prediction of behavior. In W. Crano, & P. Radmila (Eds.), Attitudes and attitude change (pp. 289-303). New York: Psychology Press.
Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11(1), 1-33. doi:https://doi.org/10.1080/14792779943000116
Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173-221). Mahwah, NJ: Erlbaum.
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228), 931-934. doi:https://doi.org/10.1016/S0140-6736(20)30567-5
Banas, K., Lyimo, R. A., Hospers, H. J., Van der Ven, A., & De Bruin, M. (2017). Predicting adherence to combination antiretroviral therapy for HIV in Tanzania: A test of an extended theory of planned behaviour model. Psychology & Health, 32(10), 1249-1265. doi:https://doi.org/10.1080/08870446.2017.1283037
Bocchini, B. (2020). Coronavírus: Pesquisa mostra que 50% dos médicos acusam falta de EPI. Agência Brasil, 28 de Abril. Recuperado de https://agenciabrasil.ebc.com.br/geral/noticia/2020-04/coronavirus-pesquisa-mostra-que-50-dos-medicos-acusam-falta-de-epi
Bogg, T., & Milad, E. (2020). Slowing the Spread of COVID-19: Demographic, personality, and social cognition predictors of guideline adherence in a representative US sample. PsyArXiv, 3 de Abril. doi:https://doi.org/10.31234/osf.io/yc2gq
Brinol, P., & Petty, R. E. (2009). Source factors in persuasion: A self-validation approach. European Review of Social Psychology, 20(1), 49-96. doi:https://doi.org/10.1080/10463280802643640
Canabarro, A., Tenorio, E., Martins, R., Martins, L., Brito, S., & Chaves, R. (2020). Data-driven study of the COVID-19 pandemic via age-structured modelling and prediction of the health system failure in Brazil amid diverse intervention strategies. medRxiv, 15 de abril. doi:https://doi.org/10.1101/2020.04.03.20052498
Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S. C., & Di Napoli, R. (2020). Features, evaluation and treatment coronavirus (COVID-19). In Statpearls [internet]. StatPearls Publishing.
Centers for Disease Control and Prevention (2020, Maio). Social Distancing: Keep Your Distance to Slow the Spread. Recuperado de https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/social-distancing.html
Gana, K., & Broc G. (2019). Structural equation modeling with lavaan. London: Iste & Wiley.
Hoefnagel, J. G., Massar, K., & Hautvast, J. L. (2019). Non-adherence to malaria prophylaxis: The influence of travel-related and psychosocial factors. Journal of Infection and Public Health, 13(4), 532-537. doi:https://doi.org/10.1016/j.jiph.2019.10.004
In Loco. (2020). Mapa Brasileiro da COVID-19. Recuperado de https://www.inloco.com.br/pt/covid-19
Liddelow, C., Mullan, B., & Novoradovskaya, E. (2020). Exploring medication adherence amongst Australian adults using an extended theory of planned behaviour. International Journal of Behavioral Medicine, Publicação eletrônica antecipada. doi:https://doi.org/10.1007/s12529-020-09862-z
Lurie, N., Saville, M., Hatchett, R., & Halton, J. (2020). Developing Covid-19 vaccines at pandemic speed. New England Journal of Medicine, 382(21), 1969-1973. doi:https://doi.org/10.1056/NEJMp2005630
McEachan, R. R. C., Conner, M., Taylor, N. J., & Lawton, R. J. (2011). Prospective prediction of health-related behaviours with the theory of planned behaviour: A meta-analysis. Health Psychology Review, 5(2), 97-144. doi:https://doi.org/10.1080/17437199.2010.521684
Milne, G. J., & Xie, S. (2020). The effectiveness of social distancing in mitigating COVID-19 spread: A modelling analysis. medRxiv, 23 de Março. doi:https://doi.org/10.1101/2020.03.20.20040055
Petty, R. E., Fazio, R. H., & Briñol, P. (2008). Attitudes: Insights from the new implicit measures. New York, NY: Psychology Press.
Petty, R. E., Briñol, P., & Priester, J. R. (2009). Mass media attitude change: Implications of the elaboration likelihood model of persuasion. In J. Bryant, & M. B. Oliver (Eds.), Media effects: Advances in theory and research (pp. 125-164). New York: Routledge.
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Recuperado de https://www.R-project.org/
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 48(2), 1-36.
Rothan, H. A., & Byrareddy S. N. (2020). The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. Journal of Autoimmunity, 109, on line. doi:https://doi.org/10.1016/j.jaut.2020.102433
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (Sixth Edition). Boston: Pearson.
Vecchione, M., Natali, E. M., & Fida, R. (2013). L’analise di variabili categoriali e non normali. In C. Barbaranelli, S. Ingoglia (Eds.), I Modelli di Equazioni Strutturali (pp. 265-294). Milano: LED.
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (pp. 209-231). London: The Guilford Press.
World Health Organization [WHO]. (2020, Janeiro 12). Novel coronavirus: China. Recuperado de http://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/
Os artigos publicados na Revista Psicologia e Saúde têm acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições, desde que o trabalho original seja corretamente citado.