| Literature DB >> 29449785 |
Dana Lo1, Florence Wu2, Mark Chan3, Rodney Chu2, Donald Li4.
Abstract
BACKGROUND: Numerous studies around the world has already suggested that burnout among doctors is a global phenomenon. However, studies for burnout in doctors are relatively limited in Chinese communities when compared to the West. As risk factors, barriers to intervention and strategies combatting burnout in different parts of the world can vary a lot due to different social culture and healthcare system, study with a focus at doctors in China from a cultural perspective is a worthful endeavor.Entities:
Year: 2018 PMID: 29449785 PMCID: PMC5806482 DOI: 10.1186/s12930-018-0040-3
Source DB: PubMed Journal: Asia Pac Fam Med ISSN: 1444-1683
Search criteria
| Prevalence terms | |
| AND | |
| Factor terms | |
| Filters | Published between January 2006 and December 2016 |
Selection criteria
| Criteria | Included | Excluded |
|---|---|---|
| Publication type | Published in peer-reviewed scientific journals | Book chapters, editorials, dissertations, theses and conference abstracts |
| Study design | Cross-sectional study design | Qualitative studies |
| Population | Practising doctors | Non-doctors (medical students, and non-doctor health care providers such as nurses and traditional Chinese medicine practitioners were excluded) |
Fig. 1Flow diagram of article selection
Characteristics of included studies
| Author/year | Study name | Design/data collection wave and year | Population/characteristics | Regions | No. of centers | Training status/specialty | Measuring instrument | Findings |
|---|---|---|---|---|---|---|---|---|
| Zhou 2016 [ | Cross-sectional survey | n = 1274 | Heilongjiang | 2 | Trained | 15-item CMBI | Score 1–7 | |
| Wen 2016 [ | Cross-sectional survey | n = 1537 | Sichuan, Chongqing, Gansu, Guizhou, Guangdong, Shanxi, Hunan, Zhejiang, Yunnan, Ningxia | 46 | Not stated | 15-item CMBI-GS | Weighed sum score equation | |
| Jin 2015 [ | Cross-sectional survey | n = 135 | Shanghai | 2 | Intern | 15-item CMBI-GS | Score 1–7 | |
| Xiao 2014 [ | Cross-sectional survey | n = 205 | Beijing | 3 | Not stated | 15-item CMBI-GS | 6 pt | |
| Wang 2014 [ | Cross-sectional survey | n = 457 | Shanghai | 21 | From junior to senior | 19-item CMBI-HSS | Score 1–7 | |
| Wu 2013 [ | Cross-sectional survey | n = 1618 | Liaoning | 7 | From junior to senior | 16-item CBMI-GS | Score 0–6 | |
| Cui 2013 [ | Cross-sectional survey | n = 510 | Beijing, Xian, Shanghai, Jiangsu, Zhejiang, Guangdong, Fujian, Hubei, Hunan, Inner Mongolia | ≥ 10 | From junior to senior | 16-item CMBI-GS | Score 0–6 | |
| Wang 2012 [ | Cross-sectional survey | n = 1011 | Liaoning | 6 | From junior to senior | 15-item CMBI-GS | Score 0–6 | |
| Zhang 2011 [ | Cross-sectional survey | n = 1451 | Hubei | 67 | From junior to senior | 15-item CMBI | Score 1–5 | |
| Wu 2008 [ | Cross-sectional survey | n = 543 | Henan | 3 | Not stated | 16-item CMBI-GS | Score 0–6 | |
| Zhu 2006 [ | Cross-sectional survey | n = 561 | Not stated | 3 | From junior to senior | 16-item CMBI-GS | Score 1–7 |
Summary of findings of the included studies
| Author/year | Prevalence of burnout | Adverse impact of burnout | Predictive factors of burnout | ||||
|---|---|---|---|---|---|---|---|
| Individual | Society | Workload | Work setting | Sociodemographic | Individual perception | ||
| Zhou 2016 [ | ✓ | ||||||
| Wen 2016 [ | 76.9% (some or serious) | ✓ | ✓ | ✓ | |||
| Jin 2015 [ | ✓ | ||||||
| Xiao 2014 [ | 25.4% (high) | ✓ | |||||
| Wang 2014 [ | 66.5% (mild or severe) | ✓ | ✓ | ✓ | ✓ | ||
| Wu 2013 [ | 12.1% (high burnout) | ✓ | ✓ | ✓ | |||
| Cui 2013 [ | 81.8–87.8% (medium or above) | ✓ | ✓ | ||||
| Wang 2012 [ | ✓ | ||||||
| Zhang 2011 [ | ✓ | ||||||
| Wu 2008 [ | ✓ | ✓ | ✓ | ||||
| Zhu 2006 [ | ✓ | ✓ | ✓ | ||||