Traditional Chinese medicine: A Bayesian network model of public awareness, usage determinants, and perception of scientific support in Austria

Introduction Despite the paucity of evidence verifying its efficacy and safety, traditional Chinese medicine (TCM) is expanding in popularity and political support. Decisions to include TCM diagnoses in the International Classification of Diseases 11th Revision (ICD-11) by the World Health Organization (WHO) and campaigns to integrate TCM into national healthcare systems have occurred whilst the public perception and usage of TCM, especially in Europe, remains undetermined. Accordingly, this study investigates the popularity, usage patterns, perception of scientific support for TCM, and its relationship to homeopathy. Methods A cross-sectional survey was performed in Austria and data from 1382 participants were analysed. A Bayesian network model retrieved partial correlations indicating distinct associations between sociodemographic determinants, complementary and alternative medicine (CAM) usage patterns, readiness to vaccinate, and TCM related variables. Results TCM was broadly known by the Austrian population (89.9% of women, 90.6% of men), with 58.9% of women and 39.5% of men using TCM between 2016 and 2019. 66.4% of women and 49.7% of men agreed with TCM being supported by science. We found a strong positive relationship between the perceived scientific support for TCM and trust in TCM-certified medical doctors. Moreover, perceived scientific support for TCM was negatively correlated with the proclivity to get vaccinated. Additionally, our Bayesian network model yielded distinct associations between TCM-, homeopathy-, and vaccination-related variables. Conclusion TCM is widely known within the Austrian general population and actively used by a substantial proportion. However, a crucial disparity exists between the commonly held public perception that TCM is scientific and findings from evidence-based studies. As public opinion towards TCM, and the proclivity to use it, are promoted through institutionalisation and official acknowledgement, it would be critical to sustain and support the distribution of unbiased, science-driven information by governmental institutions and policymakers to encourage informed patient-driven decision-making.

Framework of Electrical Fire Probability Estimation Based on Bayesian Network Model Inference

Electrical fire had become one of the main parts in total fire accidents. Most of researches rely on the complex combustion models, which consume a huge number of computational resources. However, few studies focus on evaluating fire disaster by probability theory, and estimate the likelihood of fire occurring by the calculation result of probability based on the current data from the sensor. Bayesian Network is introduced due to the advantage of calculation complexity, ability of expressing uncertain factors and the accuracy of model with incomplete data. Some problems should be solved before using Bayesian Network to inference events based on given evidences. In this paper, the structure and the parameter of the Bayesian Network is created by the discussing result of the experts and scholars in electrical fire research field. A frequently-used fuzzy function called Sigmoid function to process data from raw data to the probability. Inference result by Bayesian Network is calculated by the Variable Elimination algorithm. A case study about the simulation of analyzing the probability of electrical fire happened when the load of circuit is under the high status. Research result shows that Bayesian Network model is suitable for estimating and analyzing in the scenario of electrical fire. Model has a good robust to express probability of electrical fire probability, which is of vital importance for estimating whether the fire occurs or not, thus providing significant information and instruction for preventing electrical fire and the sustainability of the environment. Based on the simulation result, it can conclude that the Bayesian network model inference is suitable for the electrical fire estimation scenario, and the introducing of this scheme is possible for predict electrical fire.

A Bayesian network based study on determining the relationship between job stress and safety climate factors in occurrence of accidents

Background Job stress and safety climate have been recognized as two crucial factors that can increase the risk of occupational accidents. This study was performed to determine the relationship between job stress and safety climate factors in the occurrence of accidents using the Bayesian network model. Methods This cross-sectional study was performed on 1530 male workers of Asaluyeh petrochemical company in Iran. The participants were asked to complete the questionnaires, including demographical information and accident history questionnaire, NIOSH generic job stress questionnaire, and Nordic safety climate questionnaire. Also, work experience and the accident history data were inquired from the petrochemical health unit. Finally, the relationships between the variables were investigated using the Bayesian network model. Results A high job stress condition could decrease the high safety climate from 53 to 37% and increase the accident occurrence from 72 to 94%. Moreover, a low safety climate condition could increase the accident occurrence from 72 to 93%. Also, the concurrent high job stress and low safety climate could raise the accident occurrence from 72 to 93%. Among the associations between the job stress factor and safety climate dimensions, the job stress and worker’s safety priority and risk non-acceptance (0.19) had the highest mean influence value. Conclusion The adverse effect of high job stress conditions on accident occurrence is twofold. It can directly increase the accident occurrence probability and in another way, it can indirectly increase the accident occurrence probability by causing the safety climate to go to a lower level.