Evidence map›Paper›PMID 37663828›Full record

ArticleFrontiers in public health2023

Factors influencing nurse fatigue during COVID-19: regression vs. fuzzy-set qualitative comparative analysis.

Huanyu Zhang, Zhixin Liu, Junping Liu, Yajie Feng, Dandan Zou, Juan Zhao, Chen Wang, Nan Wang, Xinru Liu, Lin Wu and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 16% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
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 at 2 institutions in 1 country.

Huanyu ZhangDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Zhixin LiuDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Junping LiuDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Yajie FengDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Dandan ZouJinshan Hospital of Fudan University, Shanghai, China.
Juan ZhaoSouthwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Chen WangXinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Nan WangDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Xinru LiuDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Lin WuDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Zhaoyue LiuDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Libo LiangDepartment of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China.
Jie LiuSecond Affiliated Hospital of Harbin Medical University, Harbin, China.
Harbin Medical University · CNArmy Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nurses during COVID-19 who face significant stress and high infection risk are prone to fatigue, affecting their health and quality of patient care. A cross- sectional study of 270 nurses who went to epidemic area to support anti-epidemic was carried out Methods: A web-based cross-sectional survey of 270 nurses in China who traveled to Heihe City in Heilongjiang Province to combat the novel coronavirus epidemic. The researchers collected information on sociodemographic variables, anxiety, transition shock, professionalism, collaboration, hours of work per day, and fatigue. Regression and fuzzy-set Quality Comparative Analysis ( Results: Regression analysis showed that the psychological variables significant for fatigue, transition shock ( Conclusion: This study provides two main findings, the one is the greater transition shock experienced during COVID-19 in a new environment, low levels of professionalism, anxiety, and poor nursing teamwork situations lead anti-epidemic nurses to increased fatigue. Second, the fsQCA results showed that anxiety is sufficient for fatigue and that nurses' educational status, daily working hours, and anxiety are the most effective combination of factors.

Indexed as

COVID-19AnxietyCross-Sectional StudiesFatigueHumansPandemicsanxietyCOVID-19fatiguefsQCAnurses

Identifiers

PMID37663828
PMCPMC10470039
OpenAlexW4385982362

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.