Teoria do Comportamento Planejado como Preditora do Isolamento Social por Sars-CoV-2
DOI:
https://doi.org/10.20435/pssa.v13i4.1369Palavras-chave:
isolamento social, Sars-CoV-2, infecções por coronavírus, atitudes, comportamento social, teoria do comportamento planejadoResumo
A teoria do comportamento planejado (TCP) tem se mostrado uma preditora eficiente de comportamentos associados à saúde. Essa teoria propõe que três variáveis psicológicas predizem a intenção comportamental: atitude, normas subjetivas, percepção de controle. A intenção comportamental explica o comportamento propriamente dito. Este estudo teve o objetivo de testar o poder preditivo da TCP sobre o isolamento social diante do Sars-CoV-2. Participaram 1.139 adultos, média de idade de 35,5 anos, de todas as regiões do Brasil. Os resultados mostraram adequados índices de ajuste dos modelos preditivos da TCP sobre o isolamento social. A TCP explicou 30,7% da variância do grau de percepção de isolamento e 11,5% da variância do número de vezes que saiu de casa. Dentre os componentes da TCP, a atitude mostrou-se o fator com maior poder preditivo sobre as variáveis de isolamento social. Os resultados obtidos podem apoiar campanhas de prevenção fundamentadas na mudança de atitudes.
Referências
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/
Publicado
Como Citar
Edição
Seção
Licença
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.
.jpg)
