Evidence map›Paper›PMID 39095727›Full record

ArticleBMC nursing2024

Nurse burnout: deep connections and solutions revealed by network analysis.

Tianyi Bu, Chundi Peng, Jiangheng Liu, Xiaohui Qiu, Zhengxue Qiao, Jiawei Zhou, Siyuan Ke, Yuecui Kan, Xiaomeng Hu, Kexin Qiao and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

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

13 authors.

Tianyi Bu *Psychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Chundi Peng *Psychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Jiangheng LiuHarbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
Xiaohui QiuPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Zhengxue QiaoPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Jiawei ZhouPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Siyuan KePsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Yuecui KanPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Xiaomeng HuPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Kexin QiaoPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Xuan LiuPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China.
Depin CaoDepartment of Medical Education Management, School of Health management, Harbin Medical University, Harbin, Heilongjiang, China. caodp211@163.com.
Yanjie YangPsychology and Health Management Center, Harbin Medical University, Harbin, Heilongjiang, China. yanjie1965@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBurnout is very important to nurses' physical and mental health and career development. Current approaches to assessing nurse burnout tend to use a total score modeling paradigm to explore the impact of external factors on burnout. The purpose of this study is to delve into the item-level relationship between nurse burnout and its influencing factors at both the social and psychological levels using a network analysis approach.

methodsThis study was conducted in June 2023 and 1,005 nurses from 4 hospitals out of 8 tertiary care hospitals in Harbin were selected to participate in this study using whole cluster sampling method. Measurements included a general demographic questionnaire, Trait coping styles questionnaire, Organizational commitment questionnaire, Work-family conflict scale, Transformational leadership questionnaire, and Maslach Burnout Inventory-General Survey.

resultsOur results suggest that "C1 Time-Based work interference with family" showed the strongest centrality and bridging in the overall network. This finding underscores its centrality to burnout. Other strongest bridge symptoms included "E2 Reduced personal accomplishment" and "A1 Positive coping styles" indicating their strongest connections to other clusters.

conclusionsNurse administrators should be encouraged to pay more attention to nurses' work situations and family distress, and to help nurses in a flexible way.

Indexed as

BurnoutInfluencing factorsNetwork analysisNurse

Identifiers

PMID39095727
PMCPMC11297739

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LicenceCC BY-NC-ND
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Registered trials

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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.