Factors Affecting Posttraumatic Growth of Nurses Caring for Patients with COVID-19 in Regional Medical Centers

Purpose: This study investigates the factors affecting Posttraumatic Growth (PTG) among nurses providing care for patients with COVID-19 in regional medical centers. Methods: A total of 170 nurses from D, P, and G regional medical centers participated in this study. Data were collected through self-administered questionnaires from March 16 to March 31, 2023. IBM SPSS Statistics 27 was used for data analysis, including descriptive statistics, t-test, ANOVA, Pearson’s correlation, and hierarchical regression analysis. Results: Statistically significant differences in PTG were observed based on gender, religion, and experience in caring for patients with emerging infectious diseases. PTG was significantly correlated with resilience, social support, and deliberate rumination. Resilience (β=.22, p=.003), social support (β=.18, p=.012), and deliberate rumination (β=.46, p<.001) were identified as factors influencing the posttraumatic growth of the participants. These variables accounted for 50.1% (F=29.33, p<.001) of the variances in PTG. Conclusion: The findings of this study demonstrate the necessity of developing intervention and counseling programs aimed at enhancing deliberate rumination, resilience, and social support to promote PTG among nurses caring for patients with emerging infectious diseases.

Authors

Institutions

Publication Details

Journal
Research in Community and Public Health Nursing
Published
2025-03-31
DOI
https://doi.org/10.12799/rcphn.2024.00703
Primary Topic
Posttraumatic Stress Disorder Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors Affecting Posttraumatic Growth of Nurses Caring for Patients with COVID-19 in Regional Medical Centers

Eunsuk Choi, Jin Soo Bae
Research in Community and Public Health Nursing
Posttraumatic Stress Disorder Research
article

Factors Affecting Posttraumatic Growth of Nurses Caring for Patients with COVID-19 in Regional Medical Centers

Eunsuk Choi, Jin Soo Bae
article en

Abstract

Purpose: This study investigates the factors affecting Posttraumatic Growth (PTG) among nurses providing care for patients with COVID-19 in regional medical centers. Methods: A total of 170 nurses from D, P, and G regional medical centers participated in this study. Data were collected through self-administered questionnaires from March 16 to March 31, 2023. IBM SPSS Statistics 27 was used for data analysis, including descriptive statistics, t-test, ANOVA, Pearson’s correlation, and hierarchical regression analysis. Results: Statistically significant differences in PTG were observed based on gender, religion, and experience in caring for patients with emerging infectious diseases. PTG was significantly correlated with resilience, social support, and deliberate rumination. Resilience (β=.22, p=.003), social support (β=.18, p=.012), and deliberate rumination (β=.46, p<.001) were identified as factors influencing the posttraumatic growth of the participants. These variables accounted for 50.1% (F=29.33, p<.001) of the variances in PTG. Conclusion: The findings of this study demonstrate the necessity of developing intervention and counseling programs aimed at enhancing deliberate rumination, resilience, and social support to promote PTG among nurses caring for patients with emerging infectious diseases.

Research in Community and Public Health NursingVol. 36
Daegu Catholic University Medical Center (KR)
Openalex Percentile: Top 91%
Posttraumatic Stress Disorder Research
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.