The aim of the present study was to examine the characteristics of early career psychiatrists’ (ECP) work in Russia and to assess the prevalence and severity of burnout in them. Material and methods. The Early Career Psychiatrists Council of the Russian Society of Psychiatrists conducted an anonymous online survey of ECP in Russia in July-August 2019, consisted of a structured survey and screening for professional burnout using the Maslach Burnout Inventory (MBI). The final sample consisted of 165 people. Results. A high level of burnout according to at least one of the MBI scales was revealed in 79 (78.2%) women and 39 (60.9%) men (p=0.017). Mean values of the MBI Emotional Exhaustion scale corresponded to 23.33±8.97 and 17.97±8.49 (p=0.003), the MBI Depersonalization scale — 10.46±4.81 and 9.16±4.22 (p=0.083), and the MBI Personal Accomplishment scale — 33.02±5.98 and 35.32±5.75 (p=0.026) for women and men, respectively. The following risk factors for professional burnout were identified: female sex, overlapping of several working positions, difficulties in work due to changes in documentation requirements introduced since the start of professional career. Protective factors of burnout development were: work in private clinic, satisfaction with work atmosphere, subjective estimation of quality of life as above average or as good as possible. Conclusion. Burnout was established in 71.5% of ECP in Russia, which corresponds to one of the highest burnout rates in psychiatrists according to international and national studies. Further research is needed to assess the prevalence and severity and ways to prevent burnout in psychiatrists in Russia.

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Background: the delay in language development is characterized by qualitative and quantitative underdevelopment of the vocabulary and the lack of formation of expressive speech. This violation belongs to the mildest speech pathologies, however, there is a high probability of the presence of concomitant mental pathology and the occurrence of adaptation problems at school age. In the etiology of delayed language development, its multifactorial nature has been established. Thus, there is a need to develop a tool that predicts the formation of a delay in speech development in children for the timely implementation of preventive measures.Aim of the study: to develop a tool for predicting speech development delay in children under one year old using artificial intelligence algorithms.Patients and methods: 196 children were examined. The mean age was 26.9 months (SD ± 5.5 months). The sample was divided into two groups: the first included patients with delayed speech development (n = 98), the second included children with normal speech development (n = 98). Speech status was assessed using a questionnaire to determine the speech development of a child aged 18 to 36 months (Language Development Survey). In assessing the risk factors for the occurrence of speech development delay, the “Anamnestic Card of the child” was used. To create a neural network that predicts speech delay in children under one year old, a model was developed and trained using the Keras library for the Python 3.0 programming language.Results: the analysis of the accuracy of the neural network showed a high result — 89% of the cases during the training of the model were identified correctly. At the same time, the sensitivity of the model on the test sample was 100%, and the specificity was 90%.Conclusions: the developed method can be used to create a tool for predicting speech development delay in children up to 3 years of age, which will allow for differentiated therapeutic and preventive measures that contribute to the harmonious development of the child.

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