Evidence map›Paper›PMID 38438971›Full record

ArticleBMC public health2024

Factors associated with burnout among frontline nurses in the post-COVID-19 epidemic era: a multicenter cross-sectional study.

Shitao Wang, Guoshuai Luo, XiangQian Ding, Xuelu Ma, Fei Yang, Mengen Zhang, Guangxin Sun, Fei Wang, Liping Zhu, Shuo Wang and 1 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
20.4field-weighted citation impact, top 1% of its field
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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

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  11. Burnout Among Hospital Nurses in Kazakhstan.Nursing reports (Pavia, Italy) · 2025
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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

11 authors at 7 institutions in 1 country.

Shitao Wang *Department of Neurology, Affiliated Fuyang People's Hospital of Anhui Medical University, Fuyang, China. wangshitaomd@163.com.
Guoshuai Luo *Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China.
XiangQian Ding *Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, China.
Xuelu MaDepartment of Neurology, Affiliated Fuyang People's Hospital of Anhui Medical University, Fuyang, China.
Fei YangDepartment of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Mengen ZhangDepartment of Neurology, Affiliated Fuyang People's Hospital of Anhui Medical University, Fuyang, China.
Guangxin SunDepartment of Neurology, Affiliated Fuyang People's Hospital of Anhui Medical University, Fuyang, China.
Fei WangDepartment of Neurology, Affiliated Fuyang People's Hospital of Anhui Medical University, Fuyang, China.
Liping ZhuYa'an People's Hospital, Sichuan University, Yaan, China.
Shuo WangLaboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China.
Zongyou LiDepartment of Neurology, Affiliated Fuyang People's Hospital of Anhui Medical University, Fuyang, China.
Anhui Medical University · CNFuyang Second People's Hospital · CNAffiliated Hospital of North Sichuan Medical College · CNQilu Hospital of Shandong University · CNSichuan University · CNTianjin Anding Hospital · CNTianjin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has significantly increased the risk of burnout among frontline nurses. However, the prevalence of burnout and its associated factors in the post-pandemic era remain unclear. This research aims to investigate burnout prevalence among frontline nurses in the post-pandemic period and pinpoint associated determinants in China.

methodsFrom April to July 2023, a cross-sectional study was carried out across multiple centers, focusing on frontline nurses who had been actively involved in the COVID-19 pandemic. The data collection was done via an online platform. The Maslach Burnout Inventory-Human Services Survey was utilized to evaluate symptoms of burnout. A multivariable logistic regression analysis was used to pinpoint factors associated with burnout.

resultsOf the 2210 frontline nurses who participated, 75.38% scored over the cut-off for burnout. Multivariable logistic regression revealed that factors like being female [odds ratio (OR) = 0.41, 95%CI = 0.29-0.58] and exercising 1-2 times weekly[OR = 0.53, 95%CI = 0.42-0.67] were protective factors against burnout. Conversely, having 10 or more night shifts per month[OR = 1.99, 95%CI = 1.39-2.84], holding a master's degree or higher[OR = 2.86, 95% CI = 1.59-5.15], poor health status[OR = 2.43, 95% CI = 1.93-3.08] and [OR = 2.82, 95%CI = 1.80-4.43], under virus infection[OR = 7.12, 95%CI = 2.10-24.17], and elevated work-related stress[OR = 1.53, 95% CI = 1.17-2.00] were all associated with an elevated risk of burnout.

conclusionOur findings indicate that post-pandemic burnout among frontline nurses is influenced by several factors, including gender, monthly night shift frequency, academic qualifications, weekly exercise frequency, health condition, and viral infection history. These insights can inform interventions aimed at safeguarding the mental well-being of frontline nurses in the post-pandemic period.

Indexed as

COVID-19PandemicsPsychological TestsSelf ReportBurnout, PsychologicalCross-Sectional StudiesFemaleHumansMaleBurnoutCOVID-19NursePost-pandemicPrevalence

Identifiers

PMID38438971
PMCPMC10913257
OpenAlexW4392379244

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.