ПРОГНОЗИРОВАНИЕ ФОРМИРОВАНИЯ ВЫРАЖЕННОЙ ПСИХОПАТОЛОГИЧЕСКОЙ СИМПТОМАТИКИ У ВРАЧЕЙ

Abstract

In recent years, researchers have noted an increase of mental pathology among healthcare professionals. The article presents the results of a study conducted among 755 doctors, 446 of whom were on night shifts (main group), 309 did not work at night (comparison group). Age, night shifts, proffesional burnout, decreased motivation, and mental asthenia were identified as some predictors of the formation of severe psychopathological symptoms in all surveyed respondents (n = 755) using binary logistic regression (MFI-20 questionnaire). A statistical model was developed as a regression equation, which makes it possible to predict the formation of severe psychopathological symptoms in doctors. The model is consistent with the initial data (according to Hosmer-Lemeshev p = 0.100) and statistically significant (χ2 = 275.952; p <0.001). According to the results of the ROC-curve plotting, the AUC indicator was 0.825 ± 0.015 (CI 95%, 0.796-0.854; p <0.001), which corresponds to a very good quality of the predictive model. The sensitivity of the developed prognostic model is 71.0, specificity is 77.0. Marriage, chronotype (MEQ-SA questionnaire), decreased motivation (MFI-20 questionnaire), level of emotional burnout and General TS sleep assessment (PIRS questionnaire) were identified as some predictors of the formation of severe psychopathological symptoms in doctors working during nights (n = 446), using binary logistic regression. A statistical model was developed as a regression equation, which makes it possible to predict the formation of severe psychopathological symptoms in doctors working during nights. The model is consistent with the initial data (according to Hosmer-Lemeshev p = 0.819) and statistically significant (χ2 = 211.985; p <0.001). According to the results of the ROC-curve plotting, the AUC indicator was 0.863 ± 0.017 (CI 95%, 0.830-0.896; p <0.001), which corresponds to a very good quality of the predictive model. The sensitivity of the developed prognostic model is 74.9, the specificity is 80.7.