A Cluster Analysis of Professional Quality of Life and Post-Traumatic Growth Amongst Medical Social Workers in Mainland China

Abstract Medical social workers (MSWs) engage in complex and demanding work that inevitably affects their professional well-being. A public health disaster like coronavirus disease 2019 (COVID-19) exacerbates the effects. This study aims to classify the professional quality of life (ProQOL) of MSWs during COVID-19 in mainland China. It further examines the relationship between ProQOL classification, post-traumatic growth (PTG), and demographic characteristics. ProQOL comprises three domains: compassionate satisfaction, burnout and secondary traumatic stress. About 345 MSWs completed questionnaires that measured their levels of ProQOL and PTG. Through a k-mean cluster analysis, three clusters of ProQOL are identified. The first type is named as the Career Satisfied group, characterised by MSWs with the highest level of compassionate satisfaction and the lowest level of secondary traumatic stress and burnout amongst the three clusters. The second cluster is called the Trauma-Thriving group, consisting of MSWs with the highest level of secondary traumatic stress amongst all. They also had the highest score for PTG. The third cluster, the Compassion Fatigue group, comprised MSWs with the highest burnout and lowest compassion satisfaction levels. The results underscore the importance of tailored interventions for each type of MSW that emerges in a public health disaster.

Authors

Institutions

Publication Details

Journal
The British Journal of Social Work
Published
2024-08-29
DOI
https://doi.org/10.1093/bjsw/bcae142
Citations
1
Primary Topic
Posttraumatic Stress Disorder Research
Type
article
Field-Weighted Citation Impact
0.64

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Cluster Analysis of Professional Quality of Life and Post-Traumatic Growth Amongst Medical Social Workers in Mainland China

Qingying Ji, Yuting Chen, Qing Cao, Fang Fu et al.
1 citations
The British Journal of Social Work
Posttraumatic Stress Disorder Research
0.64
article

A Cluster Analysis of Professional Quality of Life and Post-Traumatic Growth Amongst Medical Social Workers in Mainland China

Qingying Ji, Yuting Chen, Qing Cao, Fang Fu, Yong Hao Ng
article en
1 citations

Abstract

Abstract Medical social workers (MSWs) engage in complex and demanding work that inevitably affects their professional well-being. A public health disaster like coronavirus disease 2019 (COVID-19) exacerbates the effects. This study aims to classify the professional quality of life (ProQOL) of MSWs during COVID-19 in mainland China. It further examines the relationship between ProQOL classification, post-traumatic growth (PTG), and demographic characteristics. ProQOL comprises three domains: compassionate satisfaction, burnout and secondary traumatic stress. About 345 MSWs completed questionnaires that measured their levels of ProQOL and PTG. Through a k-mean cluster analysis, three clusters of ProQOL are identified. The first type is named as the Career Satisfied group, characterised by MSWs with the highest level of compassionate satisfaction and the lowest level of secondary traumatic stress and burnout amongst the three clusters. The second cluster is called the Trauma-Thriving group, consisting of MSWs with the highest level of secondary traumatic stress amongst all. They also had the highest score for PTG. The third cluster, the Compassion Fatigue group, comprised MSWs with the highest burnout and lowest compassion satisfaction levels. The results underscore the importance of tailored interventions for each type of MSW that emerges in a public health disaster.

The British Journal of Social WorkVol. 55(1)
Hong Kong Baptist University (HK), Fudan University (CN), Shanghai Children's Medical Center (CN), Shanghai Public Health Clinical Center (CN), University of Hong Kong (HK)
National Office for Philosophy and Social Sciences
Climate action
Openalex Percentile: Top 29%
Posttraumatic Stress Disorder Research
0.64
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.