Personality traits, mental disorders and psychological suffering during the COVID-19 pandemic

ABSTRACT Introduction The present study aimed to investigate the impact of the diagnosis of mental disorder and personality traits on psychological suffering during the COVID-19 pandemic. Methods 3,633 people aged between 18 and 80 years took part in this study. Participants answered the sample characterization questionnaire, the Personality Inventory for the DSM-5 - brief form (PID-5-BF), and the Depression, Anxiety, and Stress Scale (DASS-21). Confirmatory factor analysis was conducted to validate the PS construct. Then, the theoretical model was estimated by modeling structural equations and tested for adequacy. Results All relations showed to be positive and statistically significant. The diagnosis of a mental disorder has a positive impact on all five personality traits, with the greatest coefficient being Negative Affectivity and PS. The personality traits directly impacted Psychological Suffering. Psychoticism and Detachment showed the greatest coefficients, while Antagonism, the lowest one. Conclusions The results of the final model confirm the hypotheses of this study. The knowledge of this by mental healthcare practitioners allows professionals to be aware of possible aggravating crises and pre-existing symptoms during the pandemic period.

Authors

Institutions

Publication Details

Journal
Jornal Brasileiro de Psiquiatria
Published
2025-01-01
DOI
https://doi.org/10.1590/0047-2085-2024-0045
Primary Topic
COVID-19 and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Personality traits, mental disorders and psychological suffering during the COVID-19 pandemic

Bruna Fragoso Rodrigues, Clarissa Tochetto de Oliveira, Kelmara Mendes Vieira, Vítor Crestani Calegaro et al.
Jornal Brasileiro de Psiquiatria
COVID-19 and Mental Health
article

Personality traits, mental disorders and psychological suffering during the COVID-19 pandemic

Bruna Fragoso Rodrigues, Clarissa Tochetto de Oliveira, Kelmara Mendes Vieira, Vítor Crestani Calegaro, Luca Silva Corrêa
article en

Abstract

ABSTRACT Introduction The present study aimed to investigate the impact of the diagnosis of mental disorder and personality traits on psychological suffering during the COVID-19 pandemic. Methods 3,633 people aged between 18 and 80 years took part in this study. Participants answered the sample characterization questionnaire, the Personality Inventory for the DSM-5 - brief form (PID-5-BF), and the Depression, Anxiety, and Stress Scale (DASS-21). Confirmatory factor analysis was conducted to validate the PS construct. Then, the theoretical model was estimated by modeling structural equations and tested for adequacy. Results All relations showed to be positive and statistically significant. The diagnosis of a mental disorder has a positive impact on all five personality traits, with the greatest coefficient being Negative Affectivity and PS. The personality traits directly impacted Psychological Suffering. Psychoticism and Detachment showed the greatest coefficients, while Antagonism, the lowest one. Conclusions The results of the final model confirm the hypotheses of this study. The knowledge of this by mental healthcare practitioners allows professionals to be aware of possible aggravating crises and pre-existing symptoms during the pandemic period.

Jornal Brasileiro de PsiquiatriaVol. 74
Universidade Federal de Santa Maria (BR), Universidade Federal de São Paulo (BR)
Openalex Percentile: Top 92%
COVID-19 and Mental Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.