To investigate the cognitive ability and coping strategy to mental disorders among medical workers in ear-nose-throat departments and its impact on doctor-patient relationship.
 Methods: A total of 78 medical workers (including doctors, nurses, and technicians) in ear-nose-throat departments from 10 general hospitals in Hunan Province were investigated by self-compiled questionnaire on the perspective and coping strategy to mental disorders among medical workers.
 Results: Mental disorders except depression and schizophrenia were poorly understood in respondents, and many of their coping strategies were inappropriate. Furthermore, subjects tend to avoid too much contact with psychiatric patients for being afraid of the mental disorders. The poorer understanding of mental disorders, the more inappropriate coping strategies in dealing with mental disorders (P<0.001). Moreover, there was a significant difference in inappropriate coping strategies to mental disorders between patients being abused and patients not being abused (P=0.017). Factors such as education background (P=0.031) and the hospital level (P=0.038) also impacted the coping strategies to mental disorders.
 Conclusion: Among all mental disorders, only depression and schizophrenia are coped with the right way in medical workers of ear-nose-throat departments. In addition, obviously negative attitude and avoidance are found in dealing with mental disorders by medical workers. Importantly, poor cognitive ability to mental disorders is the main reason for hurting doctor-patient relationship in the ear-nose-throat departments.
Adaptation, Psychological ; Cognition ; Humans ; Nose ; Pharynx ; Physician-Patient Relations

OBJECTIVES: Firefighters are prone to suffer from psychological trauma and post-traumatic stress disorder (PTSD) in the workplace, and have a poor prognosis after PTSD. Reliable models for predicting PTSD allow for effective identification and intervention for patients with early PTSD. By collecting the psychological traits, psychological states and work situations of firefighters, this study aims to develop a machine learning algorithm with the aim of effectively and accurately identifying the onset of PTSD in firefighters, as well as detecting some important predictors of PTSD onset. METHODS: This study conducted a cross-sectional survey through convenient sampling of firefighters from 20 fire brigades in Changsha, which were evenly distributed across 6 districts and Changsha County, with a total of 628 firefighters. We used the synthetic minority oversampling technique (SMOTE) to process data sets and used grid search to finish the parameter tuning. The predictive capability of several commonly used machine learning models was compared by 5-fold cross-validation and using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, precision, recall, and F1 score. RESULTS: The random forest model achieved good performance in predicting PTSD with an average AUC score at 0.790. The mean accuracy of the model was 90.1%, with an F1 score of 0.945. The three most important predictors were perseverance, forced thinking, and reflective deep thinking, with weights of 0.165, 0.158, and 0.152, respectively. The next most important predictors were employment time, psychological power, and optimism. CONCLUSIONS PTSD onset prediction model for Changsha firefighters constructed by random forest has strong predictive ability, and both psychological characteristics and work situation can be used as predictors of PTSD onset risk for firefighters. In the next step of the study, validation using other large datasets is needed to ensure that the predictive models can be used in clinical setting.
Humans ; Stress Disorders, Post-Traumatic/diagnosis* ; Firefighters/psychology* ; Cross-Sectional Studies ; Algorithms ; Machine Learning

OBJECTIVES: Major depressive disorder (MDD) patients with anhedonia tend to have a poor prognosis. The underlying imaging basis for anhedonia in MDD remains largely unknown. The relationship between nodal properties and anhedonia in MDD patients need to be further investigated. Herein, this study aims to explore differences of cerebral functional node characteristics in MDD patients with severe anhedonia (MDD-SA) and MDD patients with mild anhedonia (MDD-MA) before and after the antidepressant treatment. METHODS: Ninety participants with current MDD were recruited in this study. 24-Item Hamilton Depression Scale (HAMD-24) and Snaith-Hamilton Pleasure Scale (SHAPS) were used to assess the severity of depression and anhedonia at baseline and the end of 6-months treatment. The MDD patients who scored above the 25th percentile on the SHAPS were assigned to an MDD-SA group (n=19), while those who scored below the 25th percentile were assigned to an MDD-MA group (n=18). All patients in the 2 groups received antidepressant treatment. Functional magnetic resonance imaging (fMRI) images of all the patients were collected at baseline and the end of 6-months treatment. Graph theory was applied to analyze the patients' cerebral functional nodal characteristics, which were measured by efficiency (e_i) and degree (k_i). RESULTS: Repeated measures 2-factor ANCOVA showed significant main effects on group on the e_i and k_i values of left superior frontal gyrus (LSFG) (P=0.003 and P=0.008, respectively), and on the e_i and k_i values of left medial orbital-frontal gyrus (LMOFG) (P=0.004 and P=0.008, respectively). Compared with the MDD-MA group, the significantly higher e_i and k_i values of the LSFG (P=0.015 and P=0.021, respectively), and the significantly higher e_i and k_i values of the LMOFG (P=0.015 and P=0.037, respectively) were observed in the MDD-SA group at baseline. Meanwhile, higher SHAPS scores could result in higher e_i and k_i values of LSFG (P=0.019 and P=0.026, respectively), and higher e_i value of LMOFG (P=0.040) at baseline; higher SHAPS scores could result in higher e_i values of LSFG (P=0.049) at the end of 6-months treatment. The multiple linear regression analysis revealed that sex were negatively correlated with the e_i and k_i values of LSFG (r= -0.014, P=0.004; r=-1.153, P=0.001, respectively). The onset age of MDD was negatively correlated with the k_i value of LSFG (r=-0.420, P=0.034) at the end of 6-months treatment. We also found that SHAPS scores at baseline were positively correlated with the HAMD-24 scores (r=0.387, P=0.022) at the end of 6-months treatment. CONCLUSIONS There are obvious differences in nodal properties between the MDD-SA and the MDD-MA patients, such as the high e_i of LSFG in the MDD-SA patients, which may be associated with the severity of anhedonia. These nodal properties could be potential biomarkers for the prognosis of MDD. The increased e_i and k_i values in the LSFG of MDD-SA patients may underlie a compensatory mechanism or protective mechanism. The mechanism may be an important component of the pathological mechanism of MDD-SA. The poor prognosis in the MDD-SA patients suggests that anhedonia may predict a worse prognosis in MDD patients. Sex and onset age of MDD may affect the nodal properties of LSFG at baseline and the end of 6-months treatment.
Anhedonia ; Antidepressive Agents/therapeutic use* ; Depressive Disorder, Major/drug therapy* ; Humans ; Infant ; Infant, Newborn ; Magnetic Resonance Imaging ; Prefrontal Cortex