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Post-traumatic growth trajectories among frontline healthcare workers during the COVID-19 pandemic: A three-wave follow-up study in mainland China.
Yan, Zhang; Wenbin, Jiang; Bohan, Lv; Qian, Wu; Qianqian, Li; Ruting, Gu; Silong, Gao; Miao, Tuo; Huanting, Li; Lili, Wei.
  • Yan Z; Department of Nursing, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Wenbin J; Department of Nursing and Hospital Infection Management, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Bohan L; School of Nursing, Qingdao University, Qingdao, China.
  • Qian W; Department of Neonatology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Qianqian L; Department of Nursing, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Ruting G; Department of Nursing, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Silong G; Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Miao T; Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Huanting L; Office of Director, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Lili W; Department of Nursing, The Affiliated Hospital of Qingdao University, Qingdao, China.
Front Psychiatry ; 13: 945993, 2022.
Article in English | MEDLINE | ID: covidwho-2009906
ABSTRACT

Objectives:

The COVID-19 pandemic has taken a significant toll on people worldwide for more than 2 years. Previous studies have highlighted the negative effects of COVID-19 on the mental health of healthcare workers (HCWs) more than the positive changes, such as post-traumatic growth (PTG). Furthermore, most previous studies were cross-sectional surveys without follow-ups. This study draws on PTG follow-up during the COVID-19 outbreak at 12-month intervals for 2 years since 2020. The trajectories and baseline predictors were described.

Methods:

A convenience sampling method was used to recruit frontline nurses or doctors at the COVID-19-designated hospital who were eligible for this study. A total of 565 HCWs completed the 2 years follow-up and were used for final data analysis. The latent growth mixture models (GMM) was used to identify subgroups of participants with different PTG trajectories. Multinomial logistic regression model was used to find predictors among sociodemographic characteristics and resilience at baseline.

Results:

Four trajectory PTG types among HCWs were identified 'Persistent, "Steady increase", "High with drop", and "Fluctuated rise." Comparing the "Persistent low" type, the other three categories were all associated with older age, higher education. Furthermore, "Persistent low" was also negatively associated with resilience at baseline.

Conclusion:

The PTG of HCWs with different characteristics showed different trends over time. It is necessary to increase the measure frequency to understand the PTG status in different times. Improving HCW's resilience could help improve staff PTG.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: Front Psychiatry Year: 2022 Document Type: Article Affiliation country: Fpsyt.2022.945993

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: Front Psychiatry Year: 2022 Document Type: Article Affiliation country: Fpsyt.2022.945993