scholarly journals Association between loneliness and psychological distress: A cross-sectional study among Japanese workers during the COVID-19 pandemic

2021 ◽  
pp. 101621
Author(s):  
Yusuke Konno ◽  
Masako Nagata ◽  
Ayako Hino ◽  
Seiichiro Tateishi ◽  
Mayumi Tsuji ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Japanese Workers

scholarly journals A cross‐sectional study of socioeconomic status and treatment interruption among Japanese workers during the COVID‐19 pandemic

2021 ◽  
Vol 63 (1) ◽  
Author(s):  
Kenji Fujimoto ◽  
Tomohiro Ishimaru ◽  
Seiichiro Tateishi ◽  
Tomohisa Nagata ◽  
Mayumi Tsuji ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Treatment Interruption ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Japanese Workers

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.

scholarly journals The relation between psychological distress and medication adherence in lung transplant candidates and recipients: A cross‐sectional study

2021 ◽  
Author(s):  
Marion J. Wessels‐Bakker ◽  
Eduard A. van de Graaf ◽  
Johanna M. Kwakkel‐van Erp ◽  
Harry G. Heijerman ◽  
Wiepke Cahn ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Medication Adherence ◽  
Lung Transplant ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Transplant Candidates

P095 PSYCHOLOGICAL DISTRESS IN PATIENTS WITH CROHN'S DISEASE: A CROSS-SECTIONAL STUDY USING THE BRIEF SYMPTOM INVENTORY INSTRUMENT

2018 ◽  
Vol 154 (1) ◽  
pp. S49
Author(s):  
Shmuel Odes ◽  
Vered Slonim-Nevo ◽  
Ruslan Sergienko ◽  
Michael Friger ◽  
Doron Schwartz ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Psychological Distress ◽  
Crohn's Disease ◽  
Cross Sectional Study ◽  
Brief Symptom Inventory ◽  
Sectional Study ◽  
Cross Sectional ◽  
Symptom Inventory

scholarly journals Sources of psychological distress among primary care physicians during the COVID-19 pandemic’s first wave in Spain: a cross-sectional study

2021 ◽  
Vol 22 ◽  
Author(s):  
Ana Cebrián-Cuenca ◽  
José Joaquín Mira ◽  
Elena Caride-Miana ◽  
Antonio Fernández-Jiménez ◽  
Domingo Orozco-Beltrán
Keyword(s):  
Primary Care ◽  
Psychological Distress ◽  
Primary Care Physicians ◽  
Linear Regression Analysis ◽  
Cross Sectional Study ◽  
Multivariate Linear Regression Analysis ◽  
Sectional Study ◽  
Rt Pcr ◽  
Cross Sectional ◽  
Protective Measures

Abstract Background: The COVID-19 pandemic is affecting people worldwide. In Spain, the first wave was especially severe. Objectives: This study aimed to identify sources and levels of distress among Spanish primary care physicians (PCPs) during the first wave of the pandemic (April 2020). Methods: A cross-sectional study was conducted using a survey that included sociodemographic data, a description of working conditions related to distress [such as gaps in training in protective measures, cleaning, and hygiene procedures in work setting, unavailability of personal protective equipments (PPEs) and COVID-19 RT-PCR test, and lack of staff due to be infected] and a validated scale, the ‘Self-applied Acute Stress Scale’ (EASE). The survey was answered by a non-probability sampling of PCPs working in family healthcare centres from different regions of Spain. Analysis of variance and multivariate linear regression analysis were performed. Results: In all, out of 518 PCP participants, 123 (23.7%) obtained high psychological distress scores. Only half of them had received information about the appropriate use of PPE. PCP characteristics associated with higher levels of distress include female gender [1.69; 95% confidence interval (CI) 0.54, 2.84]; lack of training in protective measures (1.96; 95% CI 0.94, 2.99); unavailable COVID-19 RT-PCR for health care workers after quarantine or COVID-19 treatment (−0.77 (−1.52, −0.02). Reinforcing disinfection of the work environment (P < 0.05), availability of PPEs (P < 0.05), and no healthcare professional was infected (P < 0.05) were related to the lowest distress score. Conclusions: A better understanding of the sources of distress among PCPs could prevent its effect on future outbreaks.

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