scholarly journals Perceived Stress, Psychological Capital, and Psychological Distress Among Chinese Nursing Students: A Cross-Sectional Study

2021 ◽  
Author(s):  
Feifei Sun ◽  
Cuiping Xu ◽  
Jiaomei Xue ◽  
Jing Su ◽  
Qinghua Lu ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Nursing Students ◽  
Perceived Stress ◽  
Psychological Capital ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Mediating Role ◽  
Significant Difference ◽  
The Relationship

Abstract Background: Previous studies have investigated variables related to psychological distress among nurses; however, the relationship among psychological capital, perceived stress, and psychological stress is poorly understood. This cross-sectional study examined the relationship between psychological capital, psychological distress, and perceived stress, and examined the mediating role of psychological capital in the relationship between perceived stress and psychological distress.Methods: Responses to questionnaires to assess psychological capital, psychological distress, and perceived stress were collected from 369 nursing students in a tertiary hospital in Shandong Province, China.Results: There was a statistically significant difference in perceived stress among students, based on whether or not they liked the nursing profession (P<0.01). Relative to college students, undergraduates experienced significantly higher levels of perceived stress (P<0.01). Nevertheless, there were no significant differences in perceived stress between the variables of gender, place of residence, and being an only child. Psychological distress was positively correlated (r=0.632, p<0.001), whereas psychological capital was negatively correlated, with perceived stress (r=-0.662, p<0.001). Psychological capital played a potential mediating role in the relationship between psychological distress and perceived stress.Conclusions: This study revealed the importance of psychological capital in reducing perceived stress to decrease psychological distress among Chinese nursing students. Managers should take meaningful steps to improve nursing students’ psychological capital and thereby reduce the negative impact of psychological distress.

scholarly journals Perceived stress, life events, fatigue and temperament in patients with psoriasis

2019 ◽  
Vol 47 (9) ◽  
pp. 4284-4291 ◽  
Author(s):  
Esra Pancar Yuksel ◽  
Dilek Durmus ◽  
Gokhan Sarisoy
Keyword(s):  
Life Events ◽  
Perceived Stress ◽  
Cross Sectional Study ◽  
Healthy Controls ◽  
Sectional Study ◽  
Cross Sectional ◽  
Stress Scale ◽  
Significant Difference ◽  
Healthy Control ◽  
The Relationship

Objective To evaluate the perceived stress, life events, fatigue and temperament profile in patients with psoriasis and to investigate the relationship between these factors. Methods This cross-sectional study included patients with psoriasis and healthy control subjects. The two groups were compared regarding the number of life events, Perceived Stress Scale (PSS) and Multidimensional Assessment of Fatigue scores. The Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire was used to evaluate the personality traits among the two groups. Results A total of 75 patients with psoriasis (mean ± SD age, 44.94 ± 13.62 years) and 75 healthy controls (mean ± SD age, 41.10 ± 8.89 years) were included in the study. A statistically significant difference was found between the two groups in terms of the presence of life events, PSS score, fatigue and temperament profiles. Patients with psoriasis with depressive, cyclothymic and anxious temperament profiles were found to have higher PSS scores. In the psoriasis group, the PSS scores were positively correlated with the number of life events. Conclusions Stress and life events were found to be correlated with psoriasis. In the patients with psoriasis, depressive, cyclothymic and anxious temperament profiles seemed to be associated with higher perceived stress.

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

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