Communication during brief intervention, intention to change, and outcome.

OBJECTIVES: To explore the relationship between patient's intention to change regarding future alcohol consumption following brief alcohol intervention (BAI) and changes in alcohol consumption 12-months later and the communication characteristics between patient and counselor during BAI. DESIGN, SETTING AND SUBJECTS: Data from 367 patients (experimental arm) of a pragmatic randomized controlled trial were used to assess the effectiveness of BAI among hazardous drinkers attending an Emergency Department (Lausanne University Hospital, Lausanne, Switzerland). Alcohol outcome measures at baseline and 12 months follow-up included usual number of drinks per week, monthly frequency of heavy episodic drinking (5 or more standard drinks for men; 4 or more for women), and the Alcohol Use Disorders Identification Test (AUDIT) score. In addition, the communication characteristics between patient and counselor were analyzed via tape recordings using the Motivational Interviewing Skill Code (MISC) from 97 participants. Patient readiness and importance to change on a 10-point Likert scale (readiness/importance to change ruler) was asked during BAI, and patient intention to change alcohol consumption (yes/no) was asked at the last step. Differences in alcohol outcome at follow-up between the 367 patients who did or did not have an intention to change consumption at baseline were compared, as were differences between these two groups in communication characteristics for the 97 who completed tape recordings. RESULTS: Patients with an intention to decrease alcohol consumption reduced alcohol use and related problems more often, and reported higher levels of importance and readiness to change than did their counterparts. Analyses of MISC-coded data showed a significantly higher use of MI-consistent skills among those with a moderation intention, but no group differences on the 8 other counselor communication skills measures were found. Analyses of patient speech during the intervention indicated that those with an intention to change their alcohol consumption significantly more often self-explored personal ambivalence towards alcohol, expressed more intensely their ability, commitment, desire, need and reason to change their alcohol use than did those in the no decrease group. CONCLUSIONS: The intention expressed by hazardous drinkers when concluding BAI is associated with both patient change talk during BAI and drinking outcome 12 months later, but is mainly independent of counselor communication skills. This intention may be an important clinical indicator of which hazardous drinkers are most likely to improve after BAI.

Contact and communication with healthcare providers regarding influenza vaccination during the 2009-2010 H1N1 pandemic.

Objective. The existence of two vaccines seasonal and pandemic-created the potential for confusion and misinformation among consumers during the 2009-2010 vaccination season. We measured the frequency and nature of influenza vaccination communication between healthcare providers and adults for both seasonal and 2009 influenza A(H1N1) vaccination and quantified its association with uptake of the two vaccines.Methods. We analyzed data from 4040 U.S. adult members of a nationally representative online panel surveyed between March 4th and March 24th, 2010. We estimated prevalence rates and adjusted associations between vaccine uptake and vaccination-related communication between patients and healthcare providers using bivariate probit models.Results. 64.1% (95%-CI: 61.5%-66.6%) of adults did not receive any provider-issued influenza vaccination recommendation. Adults who received a provider-issued vaccination recommendation were 14.1 (95%-CI: -2.4 to 30.6) to 32.1 (95%-CI: 24.3-39.8) percentage points more likely to be vaccinated for influenza than adults without a provider recommendation, after adjusting for other characteristics associated with vaccination.Conclusions. Influenza vaccination communication between healthcare providers and adults was relatively uncommon during the 2009-2010 pandemic. Increased communication could significantly enhance influenza vaccination rates. (C) 2011 Elsevier Inc. All rights reserved.

Tree-based message diffusion for managing replicated data in unreliable and resource-constrained peer-to-peer environment

Abstract This thesis proposes a set of adaptive broadcast solutions and an adaptive data replication solution to support the deployment of P2P applications. P2P applications are an emerging type of distributed applications that are running on top of P2P networks. Typical P2P applications are video streaming, file sharing, etc. While interesting because they are fully distributed, P2P applications suffer from several deployment problems, due to the nature of the environment on which they perform. Indeed, defining an application on top of a P2P network often means defining an application where peers contribute resources in exchange for their ability to use the P2P application. For example, in P2P file sharing application, while the user is downloading some file, the P2P application is in parallel serving that file to other users. Such peers could have limited hardware resources, e.g., CPU, bandwidth and memory or the end-user could decide to limit the resources it dedicates to the P2P application a priori. In addition, a P2P network is typically emerged into an unreliable environment, where communication links and processes are subject to message losses and crashes, respectively. To support P2P applications, this thesis proposes a set of services that address some underlying constraints related to the nature of P2P networks. The proposed services include a set of adaptive broadcast solutions and an adaptive data replication solution that can be used as the basis of several P2P applications. Our data replication solution permits to increase availability and to reduce the communication overhead. The broadcast solutions aim, at providing a communication substrate encapsulating one of the key communication paradigms used by P2P applications: broadcast. Our broadcast solutions typically aim at offering reliability and scalability to some upper layer, be it an end-to-end P2P application or another system-level layer, such as a data replication layer. Our contributions are organized in a protocol stack made of three layers. In each layer, we propose a set of adaptive protocols that address specific constraints imposed by the environment. Each protocol is evaluated through a set of simulations. The adaptiveness aspect of our solutions relies on the fact that they take into account the constraints of the underlying system in a proactive manner. To model these constraints, we define an environment approximation algorithm allowing us to obtain an approximated view about the system or part of it. This approximated view includes the topology and the components reliability expressed in probabilistic terms. To adapt to the underlying system constraints, the proposed broadcast solutions route messages through tree overlays permitting to maximize the broadcast reliability. Here, the broadcast reliability is expressed as a function of the selected paths reliability and of the use of available resources. These resources are modeled in terms of quotas of messages translating the receiving and sending capacities at each node. To allow a deployment in a large-scale system, we take into account the available memory at processes by limiting the view they have to maintain about the system. Using this partial view, we propose three scalable broadcast algorithms, which are based on a propagation overlay that tends to the global tree overlay and adapts to some constraints of the underlying system. At a higher level, this thesis also proposes a data replication solution that is adaptive both in terms of replica placement and in terms of request routing. At the routing level, this solution takes the unreliability of the environment into account, in order to maximize reliable delivery of requests. At the replica placement level, the dynamically changing origin and frequency of read/write requests are analyzed, in order to define a set of replica that minimizes communication cost.

Young infants' triangular communication with their parents in the context of maternal postpartum psychosis: Four case studies

With increasing data on the dynamics of normative couples as they transition to parenthood and become a triad, the need for greater understanding of the impact of parental psychopathology on this transition has become clear. The goal of the current article is to begin exploring this area that has received little attention to date, by describing case examples from a study of clinical families as they transitioned to parenthood. Four representative cases were selected from a pool of 13 mother-father-baby triads, for whom the mother had been hospitalized conjointly with her infant due to a psychotic episode during the postpartum period. The families were observed as part of a clinical consultation that included a semistructured play paradigm known as the Lausanne Trilogue Play (LTP; E. Fivaz-Depeursinge, & A. Corboz-Warnery, 1999). Interactions were scored using standardized measures as well as clinical impressions. All families from the clinical sample were noted to struggle and frequently failed to achieve the goals of play. The impact on the infants in terms of their developing sense of self as well as their defensive strategies in this context are discussed, with clinical implications explored.

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Designing pheromone communication in swarm robotics: Group foraging behavior mediated by chemical substance

In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.