Evidence map›Paper›PMID 36466474›Full record

ArticleFrontiers in public health2022

Occupational stress and associated risk factors among 13,867 industrial workers in China.

Tenglong Yan, Fang Ji, Mingli Bi, Huining Wang, Xueting Cui, Baolong Liu, Dongsheng Niu, Leilei Li, Tian Lan, Tingting Xie and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
5.8field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 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

13 authors at 3 institutions in 1 country.

Tenglong YanBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Fang JiBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Mingli BiBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Huining WangBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Xueting CuiSchool of Public Health, North China University of Science and Technology, Tangshan, China.
Baolong LiuBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Dongsheng NiuBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Leilei LiBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Tian LanBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Tingting XieBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Jie WuCanvard College, Beijing Technology and Business University, Beijing, China.
Jue LiBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Xiaowen DingBeijing Institute of Occupational Disease Prevention and Treatment, Beijing, China.
Beijing Center for Disease Prevention and Control · CNBeijing Technology and Business University · CNNorth China University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Occupational stress is a critical global public health problem. We aimed to evaluate the prevalence of occupational stress among the workers in the electricity, heat, gas, water production and supply (EHGWPS), manufacturing, and transportation industries in Beijing, China. We explored the demographic differences in occupational stress status among workers in industrial enterprises. Methods: A cross-sectional study was conducted on 13,867 workers. The self-administered New Brief Job Stress Questionnaire was used to evaluate high occupational stress status, which includes four sub-dimensions (job stressors, stress response, social support, job stressors & social support). Multiple regression and logistic regression models were used to estimate the association between high occupational stress and the four occupational stress sub-dimensions with risk factors. Results: A total of 13,867 workers were included. The prevalence of high occupational stress was 3.3% in the EHGWPS industries, 10.3% in manufacturing, and 5.8% in transportation. The prevalence of high occupational stress was higher than in the other two categories ( Conclusion: Male workers with lower educational levels and longer working time may have an increased risk of occupational stress.

Indexed as

Occupational StressAgedChinaCross-Sectional StudiesEmploymentHumansMaleRisk Factorsindustrial enterprisesoccupational stressrisk factorsthe new brief job stress questionnaireworkers

Identifiers

PMID36466474
PMCPMC9714303
OpenAlexW4309604070

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.