Evidence map›Paper›PMID 40148942›Full record

ArticleBMC nursing2025

Latent profile analysis of mental workload among emergency department nurses: a cross-sectional study.

Xiaoyan Hao, Yueguang Dai, Shengjie Jia, Shuqin Liu, Chunling Zhao, Xiaomin Liu

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. Cited by 14 papers.

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0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaoyan HaoEmergency Department, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China.
Yueguang DaiEmergency Department, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China.
Shengjie JiaEmergency Department, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China.
Shuqin LiuEmergency Department, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China.
Chunling ZhaoEmergency Department, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China.
Xiaomin LiuEmergency Department, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province, China. liuxiaomin1981@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimEmergency department nurses experience varying degrees of mental workload due to various factors. The group with medium to high levels of mental workload requires particular attention. There is limited research on the potential profiles of mental workload among emergency department nurses. The purpose of this study was to identify different potential profiles of mental workload among emergency department nurses and analyze the influencing factors.

designA cross-sectional study.

methodsThis study followed the STROBE guidelines. The NASA-TLX scale was used to assess the mental workload of emergency department nurses, and latent profile analysis was employed to identify different potential profiles of their mental workload. Statistical methods, including Pearson's chi-square test and logistic regression analysis, were performed to identify factors affecting the mental workload of emergency department nurses.

resultsA total of 305 emergency department nurses completed the survey, with the majority experiencing a moderate to severe mental workload. Mental workload was a complex and dynamic phenomenon influenced by many factors. The main factors affecting mental workload included age (year), marital status, monthly income (RMB), support style, coping style, and personality traits.

conclusionThe study suggested that mental workload among emergency department nurses is widespread in China. Nursing managers should provide targeted interventions based on the mental workload profiles of emergency department nurses. It is also crucial to enhance support from family and friends for emergency department nurses in their professional endeavors. This support should facilitate the adoption of positive coping styles to effectively address challenges. Tailored interventions, based on individual personality traits, should be implemented to reduce mental workload and promote the growth of the emergency department nursing team.

Indexed as

Coping styleEmergencyInfluencing factorsLatent profile analysisMental workloadNursePersonality traits

Identifiers

PMID40148942
PMCPMC11948793

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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.