Evidence map›Paper›PMID 33845895›Full record

ArticleBMC psychology2021

Prevalence of mental health problems and associated factors among front-line public health workers during the COVID-19 pandemic in China: an effort-reward imbalance model-informed study.

Jing Zhang, Yijing Wang, Jingdong Xu, Hua You, Yan Li, Yuan Liang, Shan Li, Lina Ma, Joseph Tak-Fai Lau, Yuantao Hao and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMC psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
7.9field-weighted citation impact, top 2% 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

33 citing papers in PubMed, 61 citations in OpenAlex.

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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

14 authors at 7 institutions in 2 countries.

Jing Zhang *School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Yijing Wang *School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Jingdong XuHubei Province Center for Disease Control and Prevention, Wuhan, 430097, China.
Hua YouSchool of Public Health, Nanjing Medical University, Nanjing, 210000, China.
Yan LiGuangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
Yuan LiangSchool of Public Health, Huazhong University of Science and Technology, Wuhan, 430074, China.
Shan LiZigong Center for Disease Control and Prevention, Zigong, 643000, China.
Lina MaHubei Province Center for Disease Control and Prevention, Wuhan, 430097, China.
Joseph Tak-Fai LauCentre for Health Behaviors Research, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
Yuantao HaoSchool of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Shilin ChenSchool of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Jing ZengSchool of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Jinghua LiSchool of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China. lijinghua3@mail.sysu.edu.cn.
Jing GuSchool of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China. gujing5@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0001-5436-2339
Sun Yat-sen University · CNWuhan Center for Disease Control and Prevention · CNChinese University of Hong Kong · HKGuangzhou Center for Disease Control and Prevention · CNHuazhong University of Science and Technology · CNNanjing Medical University · CNZigong First People's Hospital · CN

Funding

the National Science and Technology Major Project of China 2018ZX10715004
6 · The paper itself

Abstract

backgroundPoor mental health status and associated risk factors of public health workers have been overlooked during the COVID-19 pandemic. This study used the effort-reward imbalance model to investigate the association between work-stress characteristics (effort, over-commitment, reward) and mental health problems (anxiety and depression) among front-line public health workers during the COVID-19 pandemic in China.

methodsA total of 4850 valid online questionnaires were collected through a self- constructed sociodemographic questionnaire, the adapted ERI questionnaire, the 9-item Patient Health Questionnaire (PHQ-9) and the 7-item General Anxiety Disorder Scale (GAD-7). Hierarchical logistic regression analysis was conducted to investigate the association between ERI factors and mental health problems (i.e., depression and anxiety), with reward treated as a potential moderator in such associations.

resultsThe data showed that effort and over-commitment were positively associated with depression and anxiety, while reward was negatively associated with depression and anxiety. Development and job acceptance were the two dimensions of reward buffered the harmful effect of effort/over-commitment on depression and anxiety, whereas esteem was non-significant.

conclusionsThis study confirmed the harmful effects of effort and over-commitment on mental health among public health workers during the COVID-19 pandemic in China. Such effects could be alleviated through an appropriate reward system, especially the development and job acceptance dimensions of such a system. These findings highlight the importance of establishing an emergency reward system, comprising reasonable work-allocation mechanism, bonuses and honorary titles, a continuous education system and better career-development opportunities.

Indexed as

COVID-19PandemicsChinaCross-Sectional StudiesDepressionHumansMental HealthPrevalencePublic HealthRewardSARS-CoV-2Stress, PsychologicalSurveys and QuestionnairesAnxietyDepressionEffort–reward imbalancePublic health workerReward

Identifiers

PMID33845895
PMCPMC8040352
OpenAlexW3155116480

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.