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RSS FeedsIJERPH, Vol. 17, Pages 741: Burnout Syndrome and Meta-Analyses: Need for Evidence-Based Research in Occupational Health. Comments on Prevalence of Burnout in Medical and Surgical Residents: A Meta-Analysis. Int. J. Environ. Res. Public. Health. 2019, 16, doi:10.3390/ijerph16091479 (International Journal of Environmental Research and Public Health)

 
 

24 january 2020 02:00:29

 
IJERPH, Vol. 17, Pages 741: Burnout Syndrome and Meta-Analyses: Need for Evidence-Based Research in Occupational Health. Comments on Prevalence of Burnout in Medical and Surgical Residents: A Meta-Analysis. Int. J. Environ. Res. Public. Health. 2019, 16, doi:10.3390/ijerph16091479 (International Journal of Environmental Research and Public Health)
 


In their meta-analysis of observational studies, Low et al. showed a high prevalence of burnout syndrome (BOS) among medical and surgical residents across the globe with an aggregate prevalence of burnout as 51.0% (CI: 45.0–57%). However, the sample size in many of the included studies was quite low (only 26 out of 47 included studies had a sample size of more than 100 participants), and almost all of the 47 studies reported a rate of respondents of less than 80% (43 out of 47, 91.4%). Furthermore, in many of them, the rate of respondents was unknown (5 out of 47) or less than 50% of eligible persons (23 out of 47 studies). As BOS is a self-reported syndrome, healthcare professionals who decided to participate in those studies were many of those affected by BOS, making the percentage of respondents potentially overstated due to the nonresponse bias. Policy decision-making in public health relies on evidence-based research; therefore, quality evaluation of studies in meta-analysis is essential to draw useful data for policymakers.


 
226 viewsCategory: Medicine, Pathology, Toxicology
 
IJERPH, Vol. 17, Pages 744: A Literature Review and Gap Analysis of Emerging Technologies and New Trends in Gambling (International Journal of Environmental Research and Public Health)
IJERPH, Vol. 17, Pages 740: Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network (International Journal of Environmental Research and Public Health)
 
 
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