Evidence map›Paper›PMID 35370822›Full record

ArticleFrontiers in psychiatry2022

Emotional Exhaustion of Burnout Among Medical Staff and Its Association With Mindfulness and Social Support: A Single Center Study During the COVID-19 Pandemic in Japan.

Makiko Sampei, Ryo Okubo, Mitsuhiro Sado, Aurelie Piedvache, Tetsuya Mizoue, Koushi Yamaguchi, Naho Morisaki

Abstract read
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Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

9 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

7 authors.

Makiko SampeiDepartment of Health Science, Health Promotion, Nippon Sport Science University, Tokyo, Japan.
Ryo OkuboTranslational Medical Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
Mitsuhiro SadoDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Aurelie PiedvacheDepartment of Social Medicine, National Research Institute for Child Health and Development, Tokyo, Japan.
Tetsuya MizoueDepartment of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan.
Koushi YamaguchiCenter of Maternal-Fetal, Neonatal and Reproductive Medicine, National Center for Child Health and Development, Tokyo, Japan.
Naho MorisakiDepartment of Social Medicine, National Research Institute for Child Health and Development, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although higher rates of burnout have been reported during the COVID-19 pandemic, the contribution of the modifiable factors is lesser-known. We investigated how the risk of emotional exhaustion was associated with mindfulness skills and social support in a single medical center in Japan. Methods: We conducted a cross-sectional web survey on mental health for all staff of a national medical hospital from February to March 2021. We examined the association between self-rated emotional exhaustion and levels of mindfulness and social support using multivariate logistic regression. Results: Of the 830 participants, signs of emotional exhaustion were observed in 261 (31%) individuals. Among those highly exposed to the virus at work, individuals with low levels of mindfulness and social support had significantly higher odds of emotional exhaustion [OR 3.46 (95% CI; 1.48-8.09), OR; 3.08 (95% CI; 1.33-7.13), respectively] compared to those with high levels. However, among those not highly exposed to the virus, individuals with both low and moderate levels of mindfulness had significantly higher odds of emotional exhaustion. [OR 3.33 (95% CI; 2.22-5.00), OR; 2.61 (95% CI; 1.73-3.94), respectively]. Conclusion: We found that factors associated with emotional exhaustion differed by exposure to SARS-CoV-2. Building mindfulness skills can help reduce the high burden placed on the staff. Additionally, increasing social support may be useful especially for workers highly exposed to SARS-CoV-2.

Indexed as

burnoutemotional exhaustionhealthcare workermindfulnesssocial support

Identifiers

PMID35370822
PMCPMC8965002

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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.