Many countries introduced the requirement to wear masks in public spaces for containing SARS-CoV-2 making it commonplace in 2020. Up until now, there has been no comprehensive investigation as to the adverse health effects masks can cause. The aim was to find, test, evaluate and compile scientifically proven related side effects of wearing masks. For a quantitative evaluation, 44 mostly experimental studies were referenced, and for a substantive evaluation, 65 publications were found. The literature revealed relevant adverse effects of masks in numerous disciplines. In this paper, we refer to the psychological and physical deterioration as well as multiple symptoms described because of their consistent, recurrent and uniform presentation from different disciplines as a Mask-Induced Exhaustion Syndrome (MIES). We objectified evaluation evidenced changes in respiratory physiology of mask wearers with significant correlation of O2 drop and fatigue (p < 0.05), a clustered co-occurrence of respiratory impairment and O2 drop (67%), N95 mask and CO2 rise (82%), N95 mask and O2 drop (72%), N95 mask and headache (60%), respiratory impairment and temperature rise (88%), but also temperature rise and moisture (100%) under the masks. Extended mask-wearing by the general population could lead to relevant effects and consequences in many medical fields.

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A proper representation of the uncertainty involved in a prediction is an important prerequisite for the acceptance of machine learning and decision support technology in safety-critical application domains such as medical diagnosis. Despite the existence of various probabilistic approaches in these fields, there is arguably no method that is able to distinguish between two very different sources of uncertainty: aleatoric uncertainty, which is due to statistical variability and effects that are inherently random, and epistemic uncertainty which is caused by a lack of knowledge. In this paper, we propose a method for binary classification that does not only produce a prediction of the class of a query instance but also a quantification of the two aforementioned sources of uncertainty. Despite being grounded in probability and statistics, the method is formalized within the framework of fuzzy preference relations. The usefulness and reasonableness of our approach is confirmed on a suitable data set with information about patients suffering from chest pain.

Even though the explorative factor analysis yields a solution different from the American original, the confirmative factor analysis results in such a high model-fit that use of the American version is justified with respect to international multicenter studies, for which this instrument will be highly valuable.

The reported results confirm our previous study and show that the German CAARS-S/O do indeed represent a reliable and cross-culturally valid measure of current ADHD symptoms in adults.

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BackgroundAn increasing number of general practitioners (GPs) are not satisfied with their working conditions and are at risk of developing burnout symptoms. As family medicine is becoming a major subject within the medical curriculum in Germany, practicing GPs need to meet higher demands in the future, ie, treating patients and taking part in the education of medical students. Accordingly, we aimed to determine GPs’ work satisfaction and risk of burnout.Materials and methodsA survey was conducted among GPs in the region of Siegen-Wittgenstein. This area is a representative rural region in Germany. The Maslach Burnout Inventory (MBI) was used to assess the risk of burnout, while the Work Satisfaction Questionnaire (WSQ) was applied to assess work satisfaction. Canonical correlations were used to examine the association between work satisfaction and burnout in GPs.ResultsA good model fit was demonstrated for both the MBI and the WSQ. The canonical correlation analysis resulted in two statistically significant canonical functions with correlations of 0.64 (P<0.001) and 0.56 (P=0.001). The full model across all functions was significant (χ2 [18]=72.41, P<0.001). Burden and the global item in the WSQ are good predictors of emotional exhaustion, while patient care, personal rewards and professional relations seem to be good predictors of depersonalization/lack of empathy. This supports the approach to burnout as a multidimensional construct which has to be thoroughly diagnosed.ConclusionDifferential interventions tailored to GPs with specific deficits in certain areas should be delivered. GPs with a high score on emotional exhaustion would need a different intervention, as these respondents have different associations with work satisfaction than do GPs with a high score for depersonalization/low empathy. Therefore, the results of this study could contribute to the design of differential interventions aimed at ameliorating symptoms of burnout in GPs.

Our study adds substantially to the knowledge of LBP-related case-mix in primary care. Information on differential health care needs may be inferred from our study, enabling decision makers to allocate resources more appropriately and to reduce costs.