Factors explaining resilience among nepalese nurses of tertiary-level hospital experiencing COVID-19 pandemic: A cross-sectional study

Resilience is important for nurses in preventing mental health problems, promoting psychological well-being, enhancing the quality of patient care, and empowering them to effectively manage healthcare crises. Fostering resilience and identifying its explanatory factors is essential for the nursing profession while facing persistent challenges during and after the COVID-19 pandemic. Considering the scarce evidence, this study aimed to identify the current state of resilience and its explanatory factors among Nepalese nurses at a tertiary hospital during the COVID-19 pandemic. Cross-sectional research was conducted among 307 nurses (i.e., staff nurses and sisters in-charge) from tertiary hospital 'A', selected using proportionate stratified random sampling. The data were collected via a self-administered structured questionnaire using a socio-demographic and job-related information sheet and five other sets of standard, valid, and reliable instruments. The data were analyzed using descriptive statistics and inferential statistics, namely Pearson Product-Moment Correlation with multiple linear regression and path analysis. The highest percentage of respondents (51.8%) had an intermediate [i.e., neither low nor high] level of resilience. The model of resilience fits with the empirical data. Furthermore, self-efficacy, perceived social support (PSS), and compassion satisfaction (CS) were the statistically significant factors that explained 31 percent of the variance in resilience (Adjusted R2 =.31) with other non-significant factors (i.e., perceived organizational support and burnout). Additionally, PSS and CS had statistically significant positive indirect effects on resilience through self-efficacy, with their total effects. In conclusion, nurses had an intermediate level of resilience, and the resilience model aligned well with the empirical data. Therefore, hospital and nursing administration should consider these findings to design and implement targeted interventions that foster resilience. This can be achieved by preventing burnout and strengthening nurses' positive psychosocial resources, including compassion satisfaction, self-efficacy, and social and organizational support, to help them navigate the challenges of their demanding profession.

Authors

Institutions

Publication Details

Journal
PLOS mental health.
Published
2025-11-12
DOI
https://doi.org/10.1371/journal.pmen.0000468
Citations
1
Primary Topic
Resilience and Mental Health
Type
article
Field-Weighted Citation Impact
2.31
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors explaining resilience among nepalese nurses of tertiary-level hospital experiencing COVID-19 pandemic: A cross-sectional study

Rekha Timalsina, Shanta Dangol Shrestha, Sarala KC, Praneed Songwathana
1 citations
PLOS mental health.
Resilience and Mental Health
2.31
article

Factors explaining resilience among nepalese nurses of tertiary-level hospital experiencing COVID-19 pandemic: A cross-sectional study

Rekha Timalsina, Shanta Dangol Shrestha, Sarala KC, Praneed Songwathana
article en
1 citations

Abstract

Resilience is important for nurses in preventing mental health problems, promoting psychological well-being, enhancing the quality of patient care, and empowering them to effectively manage healthcare crises. Fostering resilience and identifying its explanatory factors is essential for the nursing profession while facing persistent challenges during and after the COVID-19 pandemic. Considering the scarce evidence, this study aimed to identify the current state of resilience and its explanatory factors among Nepalese nurses at a tertiary hospital during the COVID-19 pandemic. Cross-sectional research was conducted among 307 nurses (i.e., staff nurses and sisters in-charge) from tertiary hospital 'A', selected using proportionate stratified random sampling. The data were collected via a self-administered structured questionnaire using a socio-demographic and job-related information sheet and five other sets of standard, valid, and reliable instruments. The data were analyzed using descriptive statistics and inferential statistics, namely Pearson Product-Moment Correlation with multiple linear regression and path analysis. The highest percentage of respondents (51.8%) had an intermediate [i.e., neither low nor high] level of resilience. The model of resilience fits with the empirical data. Furthermore, self-efficacy, perceived social support (PSS), and compassion satisfaction (CS) were the statistically significant factors that explained 31 percent of the variance in resilience (Adjusted R2 =.31) with other non-significant factors (i.e., perceived organizational support and burnout). Additionally, PSS and CS had statistically significant positive indirect effects on resilience through self-efficacy, with their total effects. In conclusion, nurses had an intermediate level of resilience, and the resilience model aligned well with the empirical data. Therefore, hospital and nursing administration should consider these findings to design and implement targeted interventions that foster resilience. This can be achieved by preventing burnout and strengthening nurses' positive psychosocial resources, including compassion satisfaction, self-efficacy, and social and organizational support, to help them navigate the challenges of their demanding profession.

PLOS mental health.Vol. 2(11)
Prince of Songkla University (TH), Patan Academy of Health Sciences (NP), Patan Hospital (NP), Nepal Development Research Institute (NP)
Openalex Percentile: Top 9%
Resilience and Mental Health
2.31
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.