Collecting mental health data during disaster is a difficult task. The aim of this study was to compare reported sensitive information regarding the disaster and general questions on physical or psychological functioning between social network (Facebook) interview and face-to-face interview after the 2011 Fukushima nuclear disaster. Data were collected from a battery of self-reported questionnaires. The questionnaires were administered to 133 face-to-face participants and to 40 Facebook interviewees, during March-April 2011. The face-to-face interview group showed a significantly higher level of posttraumatic stress disorder (PTSD) symptoms and elevated risk for clinical level of PTSD and reported more worries about another disaster, lower life satisfaction, less perceived social support and lower self-rated health than the Facebook group. Our data may suggest that the reliability of internet surveys is jeopardized during extreme conditions such as large-scale disasters as it tends to underestimate the reactions to such events. This indicates the discrepancy from data collected in situ to data collected using social networks. The implications of these results are discussed.
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http://dx.doi.org/10.1016/j.psychres.2012.11.006 | DOI Listing |
Aging Clin Exp Res
January 2025
Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China.
Objective: This study aims to analyze adverse drug events (ADE) related to romosozumab from the second quarter of 2019 to the third quarter of 2023 from FAERS database.
Methods: The ADE data related to romosozumab from 2019 Q2 to 2023 Q3 were collected. After data normalization, four signal strength quantification algorithms were used: ROR (Reporting Odds Ratios), PRR (Proportional Reporting Ratios), BCPNN (Bayesian Confidence Propagation Neural Network), and EBGM (Empirical Bayesian Geometric Mean).
Environ Monit Assess
January 2025
School of Energy and Power Engineering, Xihua University, No. 9999 Hongguang Street, Chengdu, 610039, Sichuan Province, China.
Analysis of crop water requirement and its influencing factors are important for optimal allocation of water resources. However, research on variations of climatic factors and their contribution to wheat water requirement in Xinjiang is insufficient. In our study, daily meteorological data during 1961‒2017 in Xinjiang was collected.
View Article and Find Full Text PDFMar Biotechnol (NY)
January 2025
Key Laboratory of Efficient Utilization of Non-grain Feed Resources (Co-construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Shandong Agricultural University, Taian, Shandong, China.
In China, the red swamp crayfish (Procambarus clarkii), a notorious invasive species, has become an important economic freshwater species. In order to compare the genetic diversity and population structure of crayfish from northern and southern China, we collected 60 crayfish individuals from 4 crayfish populations in northern China and 2 populations in southern China for sequencing using the 2b-RAD technique. Additionally, the whole genome sequence information obtained by 2b-RAD of 90 individuals from 2 populations in northern China and 7 populations in southern China were downloaded from NCBI.
View Article and Find Full Text PDFInt J Biometeorol
January 2025
Laboratorio de Zoología, Departamento de Biotecnológicas y Ambientales, Universidad Autónoma de Guadalajara, Av. Patria 1201, Zapopan, 45129, México.
In Mexico, Neospora caninum and Toxoplasma gondii are major causes of reproductive problems in sheep. Understanding the environmental factors that influence the spread of these parasites is crucial for developing effective control strategies. The objective of this study was to identify the environmental factors associated with N.
View Article and Find Full Text PDFSupport Care Cancer
January 2025
Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
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