A Predictive Study of Resilience and Its Relationship with Academic and Work Dimensions during the COVID-19 Pandemic
Metadatos
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2020Referencia bibliográfica
San Roman-Mata,S., Zurita-Ortega, F., Puertas-Molero, P., Badicu, G., GonzálezValero, G. TÍTULO: A Predictive Study of Resilience and Its Relationship with Academic and Work Dimensions during the COVID-19 Pandemic REF. REVISTA: Journal of Clinical Medicine, 9(10), 3258.
Résumé
El objetivo del presente estudio fue describir los niveles de resiliencia en una población española durante la pandemia del Coronavirus (COVID-19) y analizar las asociaciones existentes entre la alta resiliencia y los parámetros sociodemográficos, laborales y académicos. De los 1176 individuos de 18 a 67 años que participaron en este estudio descriptivo transversal, poco más de una cuarta parte mostraron baja resiliencia, casi la mitad reportó resiliencia moderada y poco más de una cuarta parte, una alta resiliencia. Los que estaban empleados tenían 2,16 veces más probabilidades de tener una alta resiliencia, mientras que los que tenían educación superior tenían 1,57 veces más probabilidades. Los que trabajaban en los servicios de emergencia tenían 1,66 veces más probabilidades, y los que tenían dependientes tenían 1,58 veces más probabilidades de tener una alta resiliencia.
Concluyendo además de las relaciones encontradas, se encontró la necesidad de mejorar los niveles de resiliencia en la población. Background: The aim of the present study was to describe the resilience levels in a Spanish population during the Coronavirus (COVID-19) pandemic and to analyze the existing associations between high resilience and socio-demographic, work, and academic parameters. Method: 1176 individuals aged 18–67 years participated in a descriptive cross-sectional study. The participants were administered the 10-item resilience scale developed by Connor-Davidson (CD-RISC-10) and an ad-hoc questionnaire that collected information on socio-demographic, work, and academic variables. Basic descriptive data were used to statistically analyze the data, and a binary logistic regression model was developed incorporating the professional occupation, academic level, whether the respondent worked in emergency services, and whether the respondent had dependents. Results: Slightly more than a quarter of the participants showed low resilience, almost half reported moderate resilience, and slightly more than a quarter had high resilience. Those who were employed were 2.16-times more likely to have high resilience, whilst those with higher education were 1.57-times more likely. Those working in emergency services were 1.66-times more likely, and those with dependents were 1.58-times more likely to have high resilience. Conclusion: In addition to the relationships found, a need to improve the resilience levels in the population was found.