Motivational interviewing and efficacy in reduction of alcohol use

Health behavior change is a topic that has been heavily researched by professionals in both the mental health and medical fields. Particularly, change related to alcohol behaviors has been extensively researched, likely due to the pervasiveness of alcohol-related deaths in the United States. Nearly 88,000 people die from alcohol-related causes annually, making it the fourth leading preventable cause of death in the United States (CDC, 2014). While there is a lack of a clear definition in the literature, health behavior change can be defined as the shift from risky behaviors to the initiation and maintenance of healthy behaviors and functional activities, and the self-management of chronic health conditions (Epsten, Miner, Nieuwenhujisen,& Zemper, 2006).. There are a large number of factors that affect the outcome of health behavior change, including, but not limited to, patients’ readiness and motivation to change, their environment, their physical and mental capabilities/limitations, and the specific technique taken to attempt to change behaviors. Given the difficulty that comes along with changing instilled behaviors, it is important to consider the different theoretical models of change and the steps people go through in the change process. One such technique used by professionals to facilitate health behavior change is called motivational interviewing. MI is a person-centered counseling style for addressing the common problem of ambivalence about change. MI works by activating patients’ own motivation for change and adherence to treatment and has yielded many positive trials in the areas of management and prevention of diseases ranging from cardiovascular disease, diabetes, hypertension, and pathological gambling (Butler, Miller, & Rollnick, 2008). Since 2002, more than 25,000 articles citing MI and 200 randomized clinical trials of MI have appeared in print (Miller & Rollnick, 2013). The clinical method of MI, first described in 1983, was initially developed as a brief intervention for problem drinking, in which patient motivation is a common obstacle to change (Butler, Miller, & Rollnick, 2008). Because of MI’s original purpose to help patients reduce alcohol consumption, the end of this paper will focus on a meta-analysis of the efficacy of MI for that particular health behavior. This paper will first discuss three different theoretical models of change to provide an understanding of the constructs and variables involved in the change process. Following this an analysis of the definition of motivational interviewing, the broad principles, core interviewing skills, and key concepts will be presented. Then, the aforementioned meta-analysis regarding the efficacy of MI in reduction of alcohol consumption, limitations, conclusions, and directions for future research will be discussed.Educational Psycholog

Friends - of a kind: America and its allies in the Second World War

Copyright @ 2006 Cambridge University PressThe Second World War continues to be an attractive subject for scholars and evenmore so for those writing for a general readership. One of the more traditional areas of focus has been the ‘Big Three’ – the alliance of the United States with Britain and the Soviet Union. Public interest in the three leaders – Churchill, Roosevelt and Stalin – remains high, and their decisions continue to resonate in the post-Cold War era, as demonstrated by continued (and often ahistorical) references to the decisions made at the Yalta Conference. Consequently, while other aspects of Second World War historiography have pushed into new avenues of exploration, that which has looked at the Grand Alliance has followed fairly conventional lines – the new Soviet bloc materials have been trawled to answer old questions and using the frames of reference that developed during the Cold War. This has left much to be said about the nature of the relationship of the United States with its great allies and the dynamics and processes of that alliance, and overlooked full and rounded analysis of the role of that alliance as the instrument of Axis defeat

Heuristics Miners for Streaming Event Data

More and more business activities are performed using information systems. These systems produce such huge amounts of event data that existing systems are unable to store and process them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. Since conventional process discovery algorithms have been defined for batch processing, it is difficult to apply them in such evolving environments. Existing algorithms cannot cope with streaming event data and tend to generate unreliable and obsolete results. In this paper, we discuss the peculiarities of dealing with streaming event data in the context of process mining. Subsequently, we present a general framework for defining process mining algorithms in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. We show how the Heuristics Miner, one of the most effective process discovery algorithms for practical applications, can be modified using this framework. Different stream-aware versions of the Heuristics Miner are defined and implemented in ProM. Moreover, experimental results on artificial and real logs are reported

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

Contributions to Context-Aware Smart Healthcare: A Security and Privacy Perspective

Les tecnologies de la informació i la comunicació han canviat les nostres vides de manera irreversible. La indústria sanitària, una de les indústries més grans i de major creixement, està dedicant molts esforços per adoptar les últimes tecnologies en la pràctica mèdica diària. Per tant, no és sorprenent que els paradigmes sanitaris estiguin en constant evolució cercant serveis més eficients, eficaços i sostenibles. En aquest context, el potencial de la computació ubiqua mitjançant telèfons intel·ligents, rellotges intel·ligents i altres dispositius IoT ha esdevingut fonamental per recopilar grans volums de dades, especialment relacionats amb l'estat de salut i la ubicació de les persones. Les millores en les capacitats de detecció juntament amb l'aparició de xarxes de telecomunicacions d'alta velocitat han facilitat la implementació d'entorns sensibles al context, com les cases i les ciutats intel·ligents, capaços d'adaptar-se a les necessitats dels ciutadans. La interacció entre la computació ubiqua i els entorns sensibles al context va obrir la porta al paradigma de la salut intel·ligent, centrat en la prestació de serveis de salut personalitzats i de valor afegit mitjançant l'explotació de grans quantitats de dades sanitàries, de mobilitat i contextuals. No obstant, la gestió de dades sanitàries, des de la seva recollida fins a la seva anàlisi, planteja una sèrie de problemes desafiants a causa del seu caràcter altament confidencial. Aquesta tesi té per objectiu abordar diversos reptes de seguretat i privadesa dins del paradigma de la salut intel·ligent. Els resultats d'aquesta tesi pretenen ajudar a la comunitat científica a millorar la seguretat dels entorns intel·ligents del futur, així com la privadesa dels ciutadans respecte a les seves dades personals i sanitàries.Las tecnologías de la información y la comunicación han cambiado nuestras vidas de forma irreversible. La industria sanitaria, una de las industrias más grandes y de mayor crecimiento, está dedicando muchos esfuerzos por adoptar las últimas tecnologías en la práctica médica diaria. Por tanto, no es sorprendente que los paradigmas sanitarios estén en constante evolución en busca de servicios más eficientes, eficaces y sostenibles. En este contexto, el potencial de la computación ubicua mediante teléfonos inteligentes, relojes inteligentes, dispositivos wearables y otros dispositivos IoT ha sido fundamental para recopilar grandes volúmenes de datos, especialmente relacionados con el estado de salud y la localización de las personas. Las mejoras en las capacidades de detección junto con la aparición de redes de telecomunicaciones de alta velocidad han facilitado la implementación de entornos sensibles al contexto, como las casas y las ciudades inteligentes, capaces de adaptarse a las necesidades de los ciudadanos. La interacción entre la computación ubicua y los entornos sensibles al contexto abrió la puerta al paradigma de la salud inteligente, centrado en la prestación de servicios de salud personalizados y de valor añadido mediante la explotación significativa de grandes cantidades de datos sanitarios, de movilidad y contextuales. No obstante, la gestión de datos sanitarios, desde su recogida hasta su análisis, plantea una serie de cuestiones desafiantes debido a su naturaleza altamente confidencial. Esta tesis tiene por objetivo abordar varios retos de seguridad y privacidad dentro del paradigma de la salud inteligente. Los resultados de esta tesis pretenden ayudar a la comunidad científica a mejorar la seguridad de los entornos inteligentes del futuro, así como la privacidad de los ciudadanos con respecto a sus datos personales y sanitarios.Information and communication technologies have irreversibly changed our lives. The healthcare industry, one of the world’s largest and fastest-growing industries, is dedicating many efforts in adopting the latest technologies into daily medical practice. It is not therefore surprising that healthcare paradigms are constantly evolving seeking for more efficient, effective and sustainable services. In this context, the potential of ubiquitous computing through smartphones, smartwatches, wearables and IoT devices has become fundamental to collect large volumes of data, including people's health status and people’s location. The enhanced sensing capabilities together with the emergence of high-speed telecommunication networks have facilitated the implementation of context-aware environments, such as smart homes and smart cities, able to adapt themselves to the citizens needs. The interplay between ubiquitous computing and context-aware environments opened the door to the so-called smart health paradigm, focused on the provision of added-value personalised health services by meaningfully exploiting vast amounts of health, mobility and contextual data. However, the management of health data, from their gathering to their analysis, arises a number of challenging issues due to their highly confidential nature. In particular, this dissertation addresses several security and privacy challenges within the smart health paradigm. The results of this dissertation are intended to help the research community to enhance the security of the intelligent environments of the future as well as the privacy of the citizens regarding their personal and health data

What is the True Cost to stay in the Hospital?

Currently, the unfortunate reality that receiving diverse health procedures can be extremely expensive is widely acknowledged and woefully accepted. However, have you inquired or been curious about the specific factors that influence the cost per day expensed by a hospital? Through examination, investigation, and evaluation operating SAS Enterprise Guide, SAS Enterprise Miner and Tableau I have attempted to arrive at a conclusion for this very question. Utilizing a 1.5 million row data set provided by Rhode Island, for the years 2003-2013, I analyzed the assorted elements conceivably bearing impact on the cost per day at a hospital. Regressions, decision trees, neural networks, ANOVA, linear models, and a countless number of visual representations genuinely assisted in attaining a robust conclusion. Overall, a number of various input variables, with unique magnitudes, such as age, services provided, year of discharge, and hospital provider sincerely shape the overarching cost per day at a hospital