Evidence map›Paper›PMID 40597126›Full record

ArticleBMC nursing2025

Workaholism among nurses in China: a nationwide cross-sectional survey.

Yan Gao, Hongmin Ye, Shuqin Hong, Xue Bai, Xiuni Gan

Abstract read
In one paragraph

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 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

5 authors.

Yan GaoNursing Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Hongmin YeNursing Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Shuqin HongNursing Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xue BaiHealth Management Centre, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xiuni GanNursing Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. 300650@cqmu.edu.cn.ORCID https://orcid.org/0000-0001-9854-7247

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAnxiety and depression among nursing staff, stemming from difficulties in balancing high-pressure, high-load work with daily life, are on the rise. This phenomenon may be linked to workaholism among clinical nurses, yet the prevalence of workaholism among Chinese nurses remains underreported.

objectivesTo investigate the current state of workaholism among Chinese nurses and identify factors influencing it.

methodsWe distributed study information and a survey link via WeChat to professional nursing networks. The survey included demographic characteristics and the Dutch Work Addiction Scale (DUWAS). Descriptive and comparative analyses were performed, and multiple regression analysis was performed using the workaholism score as the dependent variable.

resultsA total of 3,596 Chinese registered nurses participated in the survey. The mean workaholism total score was 29.87 (SD = 4.93), with an average item score of 2.99 (SD = 0.49). Regression analysis revealed that education level (β=0.05), position (β=0.11), professional title (β=0.10), weekly night shifts (β=0.07), and weekly work hours (β=0.15) were positively associated with workaholism. Additionally, compared to nurses without children, those with children (β=0.06) exhibited higher levels of workaholism (all P < 0.05).

conclusionsChinese nurses exhibit moderate levels of workaholism. Nursing managers can enhance occupational well-being and reduce workaholism by implementing targeted interventions focused on key factors such as educational background, professional title, position, weekly night shift frequency, average weekly working hours, and parental status. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Mental healthNursesOccupational healthWork engagement

Identifiers

PMID40597126
PMCPMC12211388

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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.