Nancedonnelly6267

Methods In this study, we examined several national United States-based injury datasets, including the web-based AgInjuryNews, the Fatality Analysis Reporting System, databases compiled by the US Consumer Product Safety Commission, and the National Fatality Review Case Reporting System. Results Our review found that these data sources cannot provide a complete picture of the incidents or the circumstantial details needed to effectively inform ORV injury prevention efforts. This is particularly true with regard to ORV-related injuries in agricultural production. Conclusions We encourage the establishment of a federally funded national agricultural injury surveillance system. However, in lieu of this, use of multiple data sources will be necessary to provide a more complete picture of ORV- and other agriculture-related injuries and fatalities.Background With the rapid development of online health communities, increasing numbers of patients and families are seeking health information on the internet. Objective This study aimed to discuss how to fully reveal the health information needs expressed by patients with hypertension in their questions in a web-based environment and how to use the internet to help patients with hypertension receive personalized health education. Methods This study randomly selected 1000 text records from the question data of patients with hypertension from 2008 to 2018 collected from Good Doctor Online and constructed a classification system through literature research and content analysis. This paper identified the background characteristics and questioning intention of each patient with hypertension based on the patient's question and used co-occurrence network analysis and the k-means clustering method to explore the features of the health information needs of patients with hypertension. Results The classification systemient education, which could help solve the problem of information asymmetry in communication between physicians and patients.Background There have been recurring reports of web-based harassment and abuse among adolescents and young adults through anonymous social networks. Objective This study aimed to explore discussions on the popular anonymous social network Yik Yak related to social and mental health messaging behaviors among college students, including cyberbullying, to provide insights into mental health behaviors on college campuses. Methods From April 6, 2016, to May 7, 2016, we collected anonymous conversations posted on Yik Yak at 19 universities in 4 different states and performed statistical analyses and text classification experiments on a subset of these messages. Results We found that prosocial messages were 5.23 times more prevalent than bullying messages. The frequency of cyberbullying messages was positively associated with messages seeking emotional help. We found significant geographic variation in the frequency of messages offering supportive vs bullying messages. Across campuses, bullying and political discussions were positively associated. We also achieved a balanced accuracy of over 0.75 for most messaging behaviors and topics with a support vector machine classifier. Conclusions Our results show that messages containing data about students' mental health-related attitudes and behaviors are prevalent on anonymous social networks, suggesting that these data can be mined for real-time analysis. find more This information can be used in education and health care services to better engage with students, provide insight into conversations that lead to cyberbullying, and reach out to students who need support.Background Stress echocardiography is a well-established diagnostic tool for suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of stress echocardiography and patients' variables including risk factors, current medication, and anthropometric variables has not been widely investigated. Objective This study aimed to use machine learning to predict significant CAD defined by positive stress echocardiography results in patients with chest pain based on anthropometrics, cardiovascular risk factors, and medication as variables. This could allow clinical prioritization of patients with likely prediction of CAD, thus saving clinician time and improving outcomes. Methods A machine learning framework was proposed to automate the prediction of stress echocardiography results. The framework consisted of four stages feature extraction, preprocessing, feature selection, and classification stage. A mutual information-based f further improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.Background Supporting women to initiate and continue breastfeeding is a global challenge. A range of breastfeeding interventions employing electronic technologies (e-technologies) are being developed, which offer different delivery modes and features over the internet; however, the impact of internet-based e-technologies on breastfeeding outcomes remains unclear. Objective This study aimed to identify the characteristics of current internet-based breastfeeding interventions employing e-technologies and investigate the effects of internet-based e-technologies on breastfeeding outcomes. Methods A systematic search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in the following databases Scopus, Web of Science, the Cochrane Database of Systematic Reviews, ScienceDirect, Google Scholar, the Association for Computing Machinery, SpringerLink, and Institute of Electrical and Electronics Engineers Xplore. Results This systematic review included 16 st the content of the proposed interventions.Background Apathy is a common symptom in neurological disorders, including dementia, and is associated with a faster rate of cognitive decline, reduced quality of life, and high caregiver burden. There is a lack of effective pharmacological treatments for apathy, and nonpharmacological interventions are a preferred first-line approach to treatment. Virtual reality (VR) using head-mounted displays (HMDs) is being successfully used in exposure- and distraction-based therapies; however, there is limited research on using HMDs for symptoms of neurological disorders. Objective This feasibility study aimed to assess whether VR using HMDs could be used to deliver tailored reminiscence therapy and examine the willingness to participate, response rates to measures, time taken to create tailored content, and technical problems. In addition, this study aimed to explore the immediate effects between verbal fluency and apathy after exposure to VR. Methods A mixed methods study was conducted in a sample of older adults residing in aged care, and 17 participants were recruited.