Network analysis of pandemic fatigue symptoms in samples from five South American countries

Author:

Caycho-Rodríguez Tomás1ORCID,Torales Julio23ORCID,Ventura-León José4,Barrios Iván5,Waisman-Campos Marcela67,Terrazas-Landivar Alexandra8,Viola Laura9,Vilca Lindsey W.10,Muñoz-del-Carpio-Toia Agueda11

Affiliation:

1. Facultad de Psicología, Universidad Científica del Sur, Lima, Peru

2. Department of Medical Psychology, School of Medical Sciences, National University of Asunción, San Lorenzo, Paraguay

3. Regional Institute for Health Research, National University of Caaguazú, Coronel Oviedo, Paraguay

4. Facultad de Ciencias de la Salud, Universidad Privada del Norte, Lima, Peru

5. Department of Statistics, School of Medical Sciences, National University of Asunción, Santa Rosa del Aguaray Campus, Santa Rosa del Aguaray, Paraguay

6. Departament of Neuropsychiatry, Fleni, Buenos Aires, Argentina

7. Universidad del Salvador, Buenos Aires, Argentina

8. Department of Psychiatry, Mental Health Center, Universidad Domingo Savio, Santa Cruz de la Sierra, Bolivia

9. Department of Child Psychiatry, Asociación Española, Montevideo. Uruguay

10. South American Center for Education and Research in Public Health, Universidad Norbert Wiener, Lima, Peru

11. Vicerrectorado de investigación, Escuela de Postgrado, Escuela de Medicina Humana, Universidad Católica de Santa María, Arequipa, Perú

Abstract

Background: Pandemic fatigue generates low motivation or the ability to comply with protective behaviors to mitigate the spread of COVID-19. Aims: This study aimed to analyze the symptoms of pandemic fatigue through network analysis in individuals from five South American countries. Method: A total of 1,444 individuals from Argentina, Bolivia, Paraguay, Peru, and Uruguay participated and were evaluated using the Pandemic Fatigue Scale. The networks were estimated using the ggmModSelect estimation method and a polychoric correlation matrix was used. Stability assessment of the five networks was performed using the nonparametric resampling method based on the case bootstrap type. For the estimation of network centrality, a metric based on node strength was used, whereas network comparison was performed using a permutation-based approach. Results: The results showed that the relationships between pandemic fatigue symptoms were strongest in the demotivation dimension. Variability in the centrality of pandemic fatigue symptoms was observed among participating countries. Finally, symptom networks were invariant and almost identical across participating countries. Conclusions: This study is the first to provide information on how pandemic fatigue symptoms were related during the COVID-19 pandemic.

Publisher

SAGE Publications

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