Evidence map›Paper›PMID 41299566›Full record

ArticleBMC nursing2025

"Watching Over and Helping Each Other": a constructivist grounded theory of collective support processes among Chinese clinical nurses during emerging infectious disease outbreaks.

Yunting Luo, Xianqiong Feng, Dandan Wang, Yafang Zheng, Xu Qiao, Shiqi Jia, Xujia Xiao, Jan D Reinhardt

Abstract read
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Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yunting LuoCenter of Infectious Diseases, West China Hospital/West China School of Nursing, Sichuan University, Chengdu, China.
Xianqiong FengDepartment of Nursing, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Nursing Key Laboratory of Sichuan Province, Chengdu, China. fengxianqiong66@126.com.
Dandan WangDepartment of Nursing, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Nursing Key Laboratory of Sichuan Province, Chengdu, China.
Yafang ZhengInstitute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China.
Xu QiaoInstitute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China.
Shiqi JiaDepartment of Nursing, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Nursing Key Laboratory of Sichuan Province, Chengdu, China.
Xujia XiaoDepartment of Nursing, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Nursing Key Laboratory of Sichuan Province, Chengdu, China.
Jan D ReinhardtInstitute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China. reinhardt@scu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmerging Infectious Diseases (EIDs) such as COVID-19 pose significant global health challenges. Nurses, as key responders in pandemics, endure substantial impacts on their physical and mental health. Thus, developing a comprehensive framework to enhance nurses’ psychological coping and resilience during public health crises is crucial.

objectiveThis study aims to understand the experiences of Chinese nurses during the COVID-19 pandemic and to establish a theoretical foundation for disaster nursing.

designThis qualitative study employs a constructivist grounded theory approach rooted in symbolic interactionism. SETTINGS AND

participantsThe research includes 53 frontline nurses from 11 provincial capitals in China, recruited between May 2020 and June 2022.

methodsUtilizing semi-structured interviews guided by existing research, the study conducted in-depth interviews. The theoretical saturation determined the sample size. Data were analysed using the constructivist grounded theory method.

resultsWe propose a dynamic three-phase process—involvement, adjustment, and integration and growth—in which nurses progress from rapid engagement under high demands, and eventually rebalancing as resources increase, to resilient practice supported by mutual helping and renewed purpose.

conclusionsThe developed conceptual model emphasizes the importance of resources, support systems, and coping strategies in addressing stressors faced by nurses. It provides a conceptual model to enhance nursing education and support systems, advocating for policies that promote resilience and well-being. The study also stresses the need to challenge persistent social stereotypes by fostering a professional, gender-neutral image.

Indexed as

Communicable diseasesCopingCOVID-19Grounded theoryInfectious disease outbreakNurses

Identifiers

PMID41299566
PMCPMC12659526

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.