Reducing the incidence and impact of peer-to-peer online trolling: a protocol for a scoping review.

INTRODUCTION: Governments worldwide are committed to reducing the prevalence of peer-to-peer online trolling. The practice of peer-to-peer online trolling, which is broadly defined as where a user intends to cause disruption or conflict online for their own amusement or advantage, is a widespread pervasive and damaging behavior, affecting over one-third of all social media users. There remains, however, a substantial barrier to addressing this behaviour due to a lack of understanding of peer-to-peer online trolling and its unique psychopathology that distinguishes it from other forms of peer-to-peer online abuse such as cyberbullying and flaming, as well as the primary information technology approach used to investigate trolling. Providing a synthesis of peer-to-peer online trolling research will assist organisations, governments and educators in addressing this deviant behaviour online. METHODS AND ANALYSIS: This protocol follows the six-stage scoping review process proposed by Arksey and O'Malley. Identifying the scoping review research question (stage 1) is followed by discussion on how studies will be selected (stage 2). We then discuss how we will determine which studies will be included in the scoping review (stage 3), as well as chart the data involved for each study included (stage 4). In stage 5, the scoping review protocol gathers, synthesises and reports the results, and consults with stakeholders about the initial protocol specifications (stage 6). ETHICS AND DISSEMINATION: As the scoping review methodology focuses on incorporating information from available publications, ethical approval is not required. An article summarising the scoping review results will be submitted for publication to a journal, presented at appropriate conferences and disseminated as part of future workshops with professionals and educators involved in reducing online trolling.

Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform.

BACKGROUND: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. OBJECTIVE: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. METHODS: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. RESULTS: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. CONCLUSIONS: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.

Relationship between obesity, insulin resistance, and coronary heart disease risk.

OBJECTIVES: The study goals were to: 1) define the relationship between body mass index (BMI) and insulin resistance in 314 nondiabetic, normotensive, healthy volunteers; and 2) determine the relationship between each of these two variables and coronary heart disease (CHD) risk factors. BACKGROUND: The importance of obesity as a risk factor for type 2 diabetes and hypertension is well-recognized, but its role as a CHD risk factor in nondiabetic, normotensive individuals is less well established. METHODS: Insulin resistance was quantified by determining the steady-state plasma glucose (SSPG) concentration during the last 30 min of a 180-min infusion of octreotide, glucose, and insulin. In addition, nine CHD risk factors: age, systolic blood pressure, diastolic blood pressure (DBP), total cholesterol, triglycerides (TG), high-density lipoprotein (HDL) cholesterol and low-density lipoprotein cholesterol concentrations, and glucose and insulin responses to a 75-g oral glucose load were measured in the volunteers. RESULTS: The BMI and the SSPG concentration were significantly related (r = 0.465, p < 0.001). The BMI and SSPG were both independently associated with each of the nine risk factors. In multiple regression analysis, SSPG concentration added modest to substantial power to BMI with regard to the prediction of DBP, HDL cholesterol and TG concentrations, and the glucose and insulin responses. CONCLUSIONS: Obesity and insulin resistance are both powerful predictors of CHD risk, and insulin resistance at any given degree of obesity accentuates the risk of CHD and type 2 diabetes.

Instrucción individual : Programa para desarrollar la aptitud de enseñanza de las enfermeras y supervisoras de salubridad

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