Evidence map›Paper›PMID 40033438›Full record

ArticleBMC psychology2025

Anxiety or reflection? exploring profiles of death awareness among chinese nurses: a latent profile analysis.

Zuming Qin, Yuting Huang, Xiaoke Zhang, Siyu Su, Huilin Zhang, Jun Peng

Abstract read
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Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zuming QinClinical Nursing Teaching and Research Section, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Yuting HuangClinical Nursing Teaching and Research Section, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Xiaoke ZhangClinical Nursing Teaching and Research Section, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Siyu SuClinical Nursing Teaching and Research Section, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Huilin Zhang *Clinical Nursing Teaching and Research Section, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. zhanghuilin3477@csu.edu.cn.
Jun Peng *School of Education city university of Macau, Macau, 999078, China. 4588775@163.com.

Funding

Hunan Province Philosophy and Social Science Project 22YBA016Natural Science Foundation of Hunan Province 2024JJ5484
6 · The paper itself

Abstract

backgroundNurses frequently encounter death due to their professional roles, impacting their perceptions of mortality. Previous research has largely focused on the negative aspect of death awareness, known as death anxiety, while the other aspect, death reflection, has been less explored. This study aims to investigate the profiles of death awareness among Chinese nurses and identify factors influencing these profiles, such as socio-demographic characteristics, work-related characteristics, death education, and resilience.

methodA cross-sectional study was conducted using a convenience sample of 2882 nurses across China. Data were collected through an online survey from September to November 2023. Latent Profile Analysis was utilized to identify distinct profiles of death anxiety and death reflection among nurses. Multinomial logistic regression was used to analyze the predictive factors for different death awareness profiles.

resultsThree distinct profiles were identified: Calm Reflectors (low death anxiety, high death reflection), The Anxious (high death anxiety, low death reflection), and Anxious Reflectors (high levels of both death anxiety and death reflection). Factors such as resilience, years of service, parental status, education level, and read death education-related information significantly predicted the classification into these profiles. Specifically, higher resilience and engagement in death education were associated with the Calm Reflectors profile.

conclusionsOur study suggests that nurses' death awareness can be categorized into three distinct profiles: Calm Reflectors, The Anxious, and Anxious Reflectors. Key predictors of these profiles include resilience, educational level, years of service, parental status, and read death education-related information. The findings suggest that enhancing resilience and targeted educational interventions are crucial for supporting nurses in managing death-related emotional and cognitive responses effectively.

Indexed as

AnxietyAttitude to DeathAwarenessNursesAdultChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedResilience, PsychologicalSurveys and QuestionnairesYoung AdultDeath anxietyDeath awarenessDeath reflectionNursesResilience

Identifiers

PMID40033438
PMCPMC11877789

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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.