scholarly journals Relationships between radiation risk perception and health anxiety, and contribution of mindfulness to alleviating psychological distress after the Fukushima accident: Cross-sectional study using a path model

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235517 ◽  
Author(s):  
Yuya Kashiwazaki ◽  
Yoshitake Takebayashi ◽  
Michio Murakami
BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  

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.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Najmieh Saadati ◽  
Poorandokht Afshari ◽  
Hatam Boostani ◽  
Maryam Beheshtinasab ◽  
Parvin Abedi ◽  
...  

Abstract Background The COVID-19 pandemic has affected many countries around the world and Iran was no exception. The aim of this study was to evaluate health anxiety of Iranian pregnant women during the COVID-19 pandemic. Methods In this cross-sectional study, 300 pregnant women in different trimesters (n = 100 in each trimester) were recruited. A demographic questionnaire and the Health Anxiety Questionnaire were used to collect data. Scores of < 27, 27–34 and more than 35 were defined as low, moderate and high health anxiety, respectively. Due to nationwide restrictions, data were collected through social media groups. Chi-square tests, ANOVA and multiple linear regression were used to analyze the data. Results Mean (SD) total anxiety scores were 22.3 ± 9.5, 24.6 ± 9.3 and 25.4 ± 10.6 in the first, second and third trimesters of pregnancy, respectively. 9, 13 and 21% of women had severe anxiety in the first, second and third trimesters of pregnancy, respectively. Women in the third trimester had significantly higher health anxiety scores than those in the first trimester (p = 0.045). Conclusion At the time of the COVID-19 pandemic, women in the second and third trimesters of pregnancy were more worried about consequences of disease, but total health anxiety scores were significantly higher among women in the third trimester of pregnancy. Health care providers should pay more attention to the mental health of pregnant women in times of crises such as the COVID-19 pandemic.


Author(s):  
Marion J. Wessels‐Bakker ◽  
Eduard A. van de Graaf ◽  
Johanna M. Kwakkel‐van Erp ◽  
Harry G. Heijerman ◽  
Wiepke Cahn ◽  
...  

2019 ◽  
Vol 7 (1) ◽  
pp. 11-16 ◽  
Author(s):  
Olumide Abiodun ◽  
Olusola Shobowale ◽  
Charles Elikwu ◽  
Daniel Ogbaro ◽  
Adebola Omotosho ◽  
...  

2018 ◽  
Vol 154 (1) ◽  
pp. S49
Author(s):  
Shmuel Odes ◽  
Vered Slonim-Nevo ◽  
Ruslan Sergienko ◽  
Michael Friger ◽  
Doron Schwartz ◽  
...  

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