Evidence map›Paper›PMID 36650448›Full record

ArticleBMC primary care2023

The status of emotional labour and its influence on job burnout among village doctors during the COVID-19 pandemic in China: a cross-sectional study.

Jia Song, Chengxin Fan, Qiusha Li, Anqi Wang, Wanchen Wang, Lifang Zhou, Haiyuan Lv, Dongping Ma, Zhongming Chen, Wenqiang Yin

Open access · goldAbstract read
In one paragraph

Article in BMC primary care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.5field-weighted citation impact, top 8% 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, 6 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

10 authors at 1 institution in 1 country.

Jia SongSchool of Management, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Chengxin FanSchool of Management, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Qiusha LiSchool of Public Health, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Anqi WangSchool of Public Health, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Wanchen WangSchool of Public Health, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Lifang ZhouSchool of Management, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Haiyuan LvSchool of Management, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Dongping MaSchool of Management, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China.
Zhongming ChenSchool of Management, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China. czm3306196@163.com.
Wenqiang YinSchool of Management, Weifang Medical University, NO. 7166, Western Baotong Road, Weifang, Shandong, China. yinwq1969@126.com.
Weifang Medical University · CN

Funding

National Natural Science Foundation of China 71804131National Natural Science Foundation of China 72004164Natural Science Foundation of Shandong Province ZR2019MG010
6 · The paper itself

Abstract

backgroundVillage doctors in China are not only the gatekeepers of rural residents' health but also the net bottom of the medical security system. However, emotional labour is increasingly threatening the stability of the rural primary medical system. In addition, the ongoing coronavirus disease 2019 (COVID-19) pandemic has further exposed the vulnerability of human resources in China's rural health system. This study aims to evaluate the current situation of emotional labour among village doctors and explore the impact of emotional labour on job burnout during the COVID-19 pandemic in China.

methodsA cross-sectional survey was conducted in December 2021 in Shandong Province. We used structured questionnaires to collect data, including sociodemographic characteristics, emotional labour, and job burnout. Data were analysed by t test, analysis of variance (ANOVA), Pearson correlation analysis, and hierarchical multiple linear regression.

resultsA total of 1,093 village doctors from Shandong Province participated in the study. More than half of the participants were male (62.40%) and were between 41 and 50 years old (53.43%). The total mean score of emotional labour was 3.17 ± 0.67, of which the surface acting (SA) score was 2.28 ± 0.90, and the deep acting (DA) score was 3.91 ± 0.93. There were significant differences in SA according to gender and work content (P < 0.05) and in DA according to gender, age, education level, and work content (P < 0.05). Pearson correlation analysis showed that SA was positively correlated with job burnout (P < 0.001), and DA was negatively correlated with job burnout (P < 0.001). Hierarchical multiple linear regression analysis revealed that 29% of the variance in job burnout is attributable to SA (β = 0.530, P < 0.001) and DA (β = -0.154, P < 0.001).

conclusionVillage doctors in Shandong Province performed moderate levels of emotional labour during the COVID-19 pandemic. SA had a significant positive effect on job burnout, while DA had a significant negative effect on job burnout among village doctors. Administrators should enhance training on emotional labour for village doctors to play a positive role in alleviating their job burnout.

Indexed as

Burnout, ProfessionalCOVID-19AdultBurnout, PsychologicalChinaCross-Sectional StudiesFemaleHumansJob SatisfactionMaleMiddle AgedPandemicsChinaCOVID-19Emotional labourJob burnoutVillage doctor

Identifiers

PMID36650448
PMCPMC9843955
OpenAlexW4316813128

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.