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The competence centre for Residency Training in Family Medicine Bavaria (CCRTB) was established to improve the quality of postgraduate medical education by offering training and mentoring programmes for residents, and by providing train-the-trainer and mentoring courses for supervisors. Beyond that, regional Residency Training Networks (RTN) on avoluntary basis were developed to facilitate structured and efficient clinical rotation programs. Primary aim was to investigate the burden of burnout and the development of professionalism among CCRTB-residencies within a cross-sectional study. Secondary aim was to evaluate differences between CCRTB-residents with and without participation in aregional RTN. Burnout was determined with the Maslach Burnout Inventory (MBI), comprising the scales emotional exhaustion, depersonalization, and personal accomplishment. Ambulatory professionalization was evaluated using the German Professional Scale (Pro-D), comprising the scales professionalism towards the patient, towards other professionals, towards society, and towards oneself. Statistical significance of group differences was calculated by nonparametric tests. Multivariable linear regression modelling was performed to estimate the independent impact of professionalization and RTN participation on burnout scores. 347 CRRTB residents in ambulatory postgraduate training were invited, 212 (61.1%) participated, and 197 (92.9%) were included in our analyses. Lower emotional exhaustion and depersonalization, and increased personal accomplishment was associated with increased professionalisation, which was significant for nearly all Pro-D scales (p ≤ 0.05). RTN residents showed higher professionalism towards the patient (p = 0.031), other professionals (p = 0.012), and towards the society (p = 0.007) than residents of unstructured programs, and higher levels of personal accomplishment (p < 0.05). Early and efficient professionalization might be akey to reduce burnout and to establish asatisfying career in family medicine. Train-the-trainer and mentoring concepts should be implemented regularly for the training of residents. Thus, increased engagement in medical didactics should be aprerequisite for accreditation as atraining practice for residents. [ABSTRACT FROM AUTHOR]

Background: Risk factors of severe COVID-19 have mainly been investigated in the hospital setting. We investigated pre-defined risk factors for testing positive for SARS-CoV-2 infection and cardiovascular or pulmonary complications in the outpatient setting. Methods: The present cohort study makes use of ambulatory claims data of statutory health insurance physicians in Bavaria, Germany, with polymerase chain reaction (PCR) test confirmed or excluded SARS-CoV-2 infection in first three quarters of 2020. Statistical modelling and machine learning were used for effect estimation and for hypothesis testing of risk factors, and for prognostic modelling of cardiovascular or pulmonary complications. Results: A cohort of 99 811 participants with PCR test was identified. In a fully adjusted multivariable regression model, dementia (odds ratio (OR) = 1.36), type 2 diabetes (OR = 1.14) and obesity (OR = 1.08) were identified as significantly associated with a positive PCR test result. Significant risk factors for cardiovascular or pulmonary complications were coronary heart disease (CHD) (OR = 2.58), hypertension (OR = 1.65), tobacco consumption (OR = 1.56), chronic obstructive pulmonary disease (COPD) (OR = 1.53), previous pneumonia (OR = 1.53), chronic kidney disease (CKD) (OR = 1.25) and type 2 diabetes (OR = 1.23). Three simple decision rules derived from prognostic modelling based on age, hypertension, CKD, COPD and CHD were able to identify high risk patients with a sensitivity of 74.8% and a specificity of 80.0%. Conclusions: The decision rules achieved a high prognostic accuracy non-inferior to complex machine learning methods. They might help to identify patients at risk, who should receive special attention and intensified protection in ambulatory care. [ABSTRACT FROM AUTHOR]