Coping with stress as a predictor of post-traumatic growth in a Peruvian population during COVID-19

• Autores: Delgado Requejo, Neicer J., Cerda Sánchez, Lourdes C., Castillo Ramos, Julio C.
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revistas - Universidad Nacional Mayor de San Marcos
• Lenguaje: español
• OAI Identifier: oai:ojs.csi.unmsm:article/24484
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/psico/article/view/24484
• Nivel de acceso: acceso abierto
• Materia: Post-traumatic growth; coping with stress; Coping strategy; coping styles; predictor, COVID-19; Crecimiento postraumático; afrontamiento al estrés; Estrategia de afrontamiento; estilos de afrontamiento

Background. The Peruvian population during Covid-19 has been exposed to stressful and traumatic events, associated with close experiences with suffering and death; but these experiences have also generated positive psychological growth. The objective was to develop a model of structural equations to determine if coping with stress is a predictor of post-traumatic growth in a Peruvian population. Method. The design was predictive cross-sectional. 310 people participated, 58 % men and 42 % women, aged 18 to 63 years (M=31.6). The COPE-28 Spanish version (Morán et al., 2010) and the Postraumatic Growth Inventory PTGI (Tedeschi & Calhoun, 1996), adapted by Ramos-Vera et al (2021). Results. In the first model, the coping strategies religion (β=.209, p<.01), self-distraction (β=.111, p<.01) and disconnection (β=-.147, p<.05) are significant predictors and explain 14.9 % of post-traumatic growth; likewise, in the second model, spiritual coping styles (β=.209, p<.01), social support (β=.172, p<.05) and cognitive (β=.164, p<.05) are important predictors that explain 14.7 % of post-traumatic growth. Conclusions. The best predictor of growth is religious coping followed by distraction and disconnection. Also, spiritual coping styles, social support, and cognitive support are important predictors.