Evidence map›Paper›PMID 36514030›Full record

ArticleBMC psychiatry2022

Learning burnout and its association with perceived stress, social support, and the Big Five personality traits in Chinese medical students during the COVID-19 pandemic: a cross-sectional study.

Simeng Wang, Honghe Li, Xin Chen, Nan Yan, Deliang Wen

Open access · goldAbstract read
In one paragraph

Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
10.6field-weighted citation impact, top 1% 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

25 citing papers in PubMed, 43 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

5 authors at 3 institutions in 1 country.

Simeng WangInstitute for International Health Professions Education and Research, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning, 110122, People's Republic of China.
Honghe LiInstitute for International Health Professions Education and Research, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning, 110122, People's Republic of China.
Xin ChenDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9 West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning, 116044, People's Republic of China.
Nan YanSchool of Medical Applied Technology, Shenyang Medical College, No.146 Huanghe Street, Yuhong District, Shenyang, Liaoning, 110034, People's Republic of China.
Deliang WenInstitute for International Health Professions Education and Research, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning, 110122, People's Republic of China. dlwen@cmu.edu.cn.
China Medical University · CNDalian Medical University · CNShenyang Medical College · CN

Funding

The Liaoning Social Science Planning Fund Project L20AED006
6 · The paper itself

Abstract

backgroundOwing to the coronavirus disease 2019, medical learning burnout has attracted increasing attention in educational research. It has a serious negative impact on medical students and their service quality. This could impair the professional development of medical students; weaken their personal and professional quality; and lead to problems such as increased medical errors and reduced patient care quality and satisfaction. This study aimed to examine the effects of perceived stress, social support, and the Big Five personality traits on learning burnout among medical students.

methodsIn November 2021, a cross-sectional survey was conducted at three medical universities in China. A self-administered questionnaire was distributed to 616 third- year students. Learning burnout, perceived stress, social support, and the Big Five personality traits (neuroticism, extroversion, openness, agreeableness, and conscientiousness) were anonymously measured. A total of 583 students were included in the final sample. Hierarchical linear regression was performed to explore the effects of perceived stress, social support, and Big Five personality traits on medical students' learning burnout.

resultsPerceived stress was positively associated with learning burnout (emotional exhaustion: ß = 0.577, p < 0.001; cynicism: ß = 0.543, p < 0.001; low professional efficacy: ß = 0.455, p < 0.001) whereas social support was negatively related with it (low professional efficacy: ß = -0.319, p < 0.001). Neuroticism had a positive effect on emotional burnout (ß = 0.152, p = 0.009). Extraversion (ß = -0.116, p = 0.006) and conscientiousness (ß = -0.363, p < 0.001) had a negative effect on low professional efficacy. Agreeableness negatively affected emotional exhaustion (ß = -0.181, p < 0.001) and cynicism (ß = -0.245, p < 0.001) and positively affected low professional efficacy (ß = 0.098, p = 0.008). The associated factors together accounted for an additional variance of learning burnout (emotional exhaustion: 39.0%; cynicism: 36.8%; low professional efficacy: 48.7%).

conclusionsSocial support is a positive resource for fighting medical students' burnout. Perceived stress was the strongest indicator of learning burnout. In addition to reducing perceived stress, developing extraversion, agreeableness, and conscientiousness should be included in burnout prevention and treatment strategies, particularly for medical students.

Indexed as

Burnout, ProfessionalCOVID-19Students, MedicalCross-Sectional StudiesEast Asian PeopleHumansPandemicsPersonalitySocial SupportSurveys and QuestionnairesCOVID-19Learning burnoutMedical studentsPerceived stressSocial supportThe Big Five personality traits

Identifiers

PMID36514030
PMCPMC9745702
OpenAlexW4311373169

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.