Evidence map›Paper›PMID 40241940›Full record

ArticleFrontiers in public health2025

Work-family conflict, overwork and mental health of female employees in China.

Jun Ma, Laixi Xu, Xuehe Zhang

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

3 authors.

Jun MaSchool of Humanities, Shandong Management University, Jinan, China.
Laixi XuAnhui Kecheng Intelligent Health Co., Ltd, Hefei, China.
Xuehe ZhangSchool of Public Affairs, University of Science and Technology of China, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The "Green Paper on the Mental Well-being of Chinese Career Women" indicates that around 85% of Career Women face mental health challenges such as anxiety, depression, and anger, with these issues being more common than in their male counterparts in China. Both work and family are identified as two major contributors to these problems. Utilizing Conservation of Resources theory, this paper examines work-family conflict and overwork as significant explanatory variables and develops a moderated mediation model to investigate the mechanisms affecting mental health issues among Chinese career women. Methods: Data were gathered through a questionnaire survey, with 500 questionnaires distributed and 393 responses received. Hayes' PROCESS macro for SPSS was employed to examine moderated mediation models, with Bootstrap resampling set at 1000. Results: (1) Work-family conflict (abbreviated as WFC) is significantly associated with emotional exhaustion (abbreviated as EE) and mental health problems in career women, with emotional exhaustion serving as a mediator between work-family conflict and mental health. (2) Overwork is positively linked to emotional exhaustion and influences the relationship between work-family conflict and emotional exhaustion. (3) Overwork also moderates the mediation effect of work-family conflict on mental health via emotional exhaustion, amplifying the mediation effect when career women are overburdened. Discussion: This study provides fresh insights into the mechanisms underlying mental health issues among career women, offering valuable information for addressing these challenges.

Indexed as

Mental HealthWork-Life BalanceAdultChinaFemaleHumansMiddle AgedSurveys and Questionnairescareer womanmental healthmoderated mediation modeloverworkwork–family conflict

Identifiers

PMID40241940
PMCPMC12000084

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.