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Background To analyze the impact of the flood disasters, social support and personality on the mental health of residents in Henan Province, China, providing fundamental knowledges for making measuring strategies to improve the psychological protection and anti-stress ability of the residents after the disaster. Material and Methods A cross-section study was conducted via an online survey platform “questionnaire star,” which included 572 residents in Henan Province, which underwent the history of ever flood disaster on July 20. The questionnaires of Impact of Event Scale-Revised Edition (IES-R), Perceived Social Support Scale (PSSS), the Depression Anxiety Stress Scales (DASS-21) and the scales of Eysenck Personality Questionnaire-Revised (EPQ-R) version in Chinese were also administered to each participant. Generalized linear regression model was performed. Results The residents who live in the flooding areas, are male and married had a significantly higher post-traumatic stress disorder (PTSD) score than their counterparts. The scores of depression-anxiety-stress in the residents with stable emotion were significantly lower than those with unstable emotion (p < 0.001). Machine learning showed that PTSD ranked the top risk factor, followed by neuroticism for Depression-Anxiety-Stress after disaster. The PTSD was negatively correlated with social support (p < 0.01), while it was positively correlated with depression-anxiety-stress and emotional stability (p < 0.01). There was a statistically significant interaction between PTSD, social support and neuroticism on depression-anxiety-stress (p < 0.001), with an independent effect of 1.4% on depression-anxiety-stress. Emotional stability showed the largest association with depression-anxiety-stress. Conclusions Residents living in the catastrophic flooding areas had significant post-traumatic mental health issues, and the severity of mental problems was differently affected by post-traumatic stress disorder and social support in individuals with different personalities. Introvert and PTSD were the major risk factors for depression-anxiety-stress after the disaster. Med Pr. 2022;73(4):305–14

Abstract Background Burnout is a health problem in nurses. Individuals with a certain personality are more susceptible to job-related burnout. General self-efficacy (GSE) is an important predictor of job-related burnout. The relationships between general self-efficacy, job-related burnout and different personality types are still not clear. This study aims to analyze the relationships of job-related burnout, stress, general self-efficacy and personality types, as well as their interactions in job-related burnout. Method A cross-sectional survey of 860 nurses was conducted between June and July 2015 in China. We measured their job-related burnout using the scale of the Maslach Occupational Burnout Scale, and personality, stress, and GSE. Machine learning of generalized linear model were performed. Results Maslach Burnout Inventory (MBI) professional efficacy was significantly associated with gender, marital status, age, job title and length of service. A machine learning algorithm showed that stress was the most important factor in job-related burnout, followed by GSE, personality type (introvert unstable), and job title. Individuals with low GSE and either introversion or unstable (high neuroticism) personality seemed to have stronger burnout when they faced stress (regardless of stress intensity) compared to others. Conclusion Stress, GSE and introvert unstable personality are the top three factors of job-related burnout. GSE moderates the effect of stress on burnout in nurses with extroversion or neuroticism personality. Reducing stress, increasing GSE, and more social support may alleviate job-related burnout in nurses. Nurses with introvert unstable personality should be given more social support in reducing stress and enhancing their GSE.