Interprofessional practices of physiotherapists working with adults with low back pain in Québec's private sector: results of a qualitative study.

BACKGROUND: Collaboration and interprofessional practices are highly valued in health systems, because they are thought to improve outcomes of care for persons with complex health problems, such as low back pain. Physiotherapists, like all health providers, are encouraged to take part in interprofessional practices. However, little is known about these practices, especially for private sector physiotherapists. This study aimed to: 1) explore how physiotherapists working in the private sector with adults with low back pain describe their interprofessional practices, 2) identify factors that influence their interprofessional practices, and 3) identify their perceived effects. METHODS: Participants were 13 physiotherapists, 10 women/3 men, having between 3 and 21 years of professional experience. For this descriptive qualitative study, we used face-to-face semi-structured interviews and conducted content analysis encompassing data coding and thematic regrouping. RESULTS: Physiotherapists described interprofessional practices heterogeneously, including numerous processes such as sharing information and referring. Factors that influenced physiotherapists' interprofessional practices were related to patients, providers, organizations, and wider systems (e.g. professional system). Physiotherapists mostly viewed positive effects of interprofessional practices, including elements such as gaining new knowledge as a provider and being valued in one's own role, as well as improvements in overall treatment and outcome. CONCLUSIONS: This qualitative study offers new insights into the interprofessional practices of physiotherapists working with adults with low back pain, as perceived by the physiotherapists' themselves. Based on the results, the development of strategies aiming to increase interprofessionalism in the management of low back pain would most likely require taking into consideration factors associated with patients, providers, the organizations within which they work, and the wider systems.

Visuospatial working memory deficits and visual pursuit impairments are not directly related in schizophrenia.

OBJECTIVE: Patients with schizophrenia show deficits in visuospatial working memory and visual pursuit processes. It is currently unclear, however, whether both impairments are related to a common neuropathological origin. The purpose of the present study was therefore to examine the possible relations between the encoding and the discrimination of dynamic visuospatial stimuli in schizophrenia. METHOD: Sixteen outpatients with schizophrenia and 16 control subjects were asked to encode complex disc displacements presented on a screen. After a delay, participants had to identify the previously presented disc trajectory from a choice of six static linear paths, among which were five incorrect paths. The precision of visual pursuit eye movements during the initial presentation of the dynamic stimulus was assessed. The fixations and scanning time in definite regions of the six paths presented during the discrimination phase were investigated. RESULTS: In comparison with controls, patients showed poorer task performance, reduced pursuit accuracy during incorrect trials and less time scanning the correct stimulus or the incorrect paths approximating its global structure. Patients also spent less time scanning the leftmost portion of the correct path even when making a correct choice. The accuracy of visual pursuit and head movements, however, was not correlated with task performance. CONCLUSIONS: The present study provides direct support for the hypothesis that active integration of visuospatial information within working memory is deficient in schizophrenia. In contrast, a general impairment of oculomotor mechanisms involved in smooth pursuit did not appear to be directly related to lower visuospatial working memory performance in schizophrenia.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).

Differences in therapeutic alliance when working with an interpreter: a preliminary study

This preliminary study aims to analyze the therapeutic alliance in a crosscultural triadic setting, where there is a therapist and a client who speak a different language but are able to interact thanks to an interpreter/cultural mediator. The participants' (therapists, clients, interpreters) representations associated with the notion of therapeutic alliance, and the level of alliance between each group was obtained and compared. Clients (N = 9) were all from Albanese origin. The results show that the three groups of participants give particular meanings to the alliance and tend to evaluate their alliance level differently. The interpreter's mediating role in the construction of the therapeutic alliance is discussed.

Remote Control of Mobile Devices in Android Platform

Remote control systems are a very useful element to control and monitor devices quickly and easily. This paper proposes a new architecture for remote control of Android mobile devices, analyzing the different alternatives and seeking the optimal solution in each case. Although the area of remote control, in case of mobile devices, has been little explored, it may provide important advantages for testing software and hardware developments in several real devices. It can also allow an efficient management of various devices of different types, perform forensic security tasks, etc ... The main idea behind the proposed architecture was the design of a system to be used as a platform which provides the services needed to perform remote control of mobile devices. As a result of this research, a proof of concept was implemented. An Android application running a group of server programs on the device, connected to the network or USB interface, depending on availability. This servers can be controlled through a small client written in Java and runnable both on desktop and web systems.

Chronic obstructive pulmonary disease in never-smoking animal farmers working inside confinement buildings

BACKGROUND: In animal farming, respiratory disease has been associated with indoor air contaminants and an excess in FEV1 decline. Our aim was to determine the characteristics and risk factors for chronic obstructive pulmonary disease (COPD) in never-smoking European farmers working inside animal confinement buildings. METHODS: A sample of participants in the European Farmers' Study was selected for a cross-sectional study assessing lung function and air contaminants. Dose-response relationships were assessed using logistic regression models. RESULTS: COPD was found in 18 of 105 farmers (45.1 SD 11.7 years) (17.1%); 8 cases (7.6%) with moderate and 3 cases (2.9%) with severe disease. Dust and endotoxin showed a dose-response relationship with COPD, with the highest prevalence of COPD in subjects with high dust (low=7.9%/high=31.6%) and endotoxin exposure (low=10.5%/high=20.0%). This association was statistically significant for dust in the multivariate analysis (OR 6.60, 95% CI 1.10-39.54). CONCLUSION: COPD in never-smoking animal farmers working inside confinement buildings is related to indoor dust exposure and may become severe. [Authors]

Alleviating Working Poverty in Postindustrial Economies

In order to identify the main social policy tools that can efficiently combat working poverty, it is essential to identify its main driving factors. More importantly, this work shows that all poverty factors identified in the literature have a direct bearing on working households through three mechanisms, namely being badly paid, having a below-average workforce participation, and high needs. One of the main purposes of this work is to assess whether the policies put forward in the specialist literature as potentially efficient really work. This is done in two ways. A first empirical prong provides an evaluation of the employment and antipoverty effects of these instruments, based on a meta-analysis of four instruments: minimum wages, tax credits for working households, family cash benefits and childcare policies. The second prong relies on a broader framework based on welfare regimes. This work contributes to the identification of a typology of welfare regimes that is suitable for the analysis of working poverty, and four countries are chosen to exemplify each regime: the US, Sweden, Germany, and Spain. It then moves on to show that the weight of the three working poverty mechanisms varies widely from one welfare regime to the other. This second empirical contribution clearly shows that there is no "one-size-fits-all" approach to the fight against working poverty. But none of this is possible without having properly defined the phenomenon. Most of the literature is characterized by a "definitional chaos" that probably does more harm than good to social policy efforts. Hence, this book provides a conceptual reflection pleading for the use of a very encompassing definition of being in work. It shows that "the working poor" is too broad a category to be used for meaningful academic or policy discussion, and that a distinction must be operated between different categories of the working poor. Failing to acknowledge this prevents the design of an efficient policy mix.

Learning in the working place: the educational potential of a multihead microscope in pathology postgraduate training.

Training future pathologists is an important mission of many hospital anatomic pathology departments. Apprenticeship-a process in which learning and teaching tightly intertwine with daily work, is one of the main educational methods in use in postgraduate medical training. However, patient care, including pathological diagnosis, often comes first, diagnostic priorities prevailing over educational ones. Recognition of the unique educational opportunities is a prerequisite for enhancing the postgraduate learning experience. The aim of this paper is to draw attention of senior pathologists with a role as supervisor in postgraduate training on the potential educational value of a multihead microscope, a common setting in pathology departments. After reporting on an informal observation of senior and junior pathologists' meetings around the multihead microscope in our department, we review the literature on current theories of learning to provide support to the high potential educational value of these meetings for postgraduate training in pathology. We also draw from the literature on learner-centered teaching some recommendations to better support learning in this particular context. Finally, we propose clues for further studies and effective instruction during meetings around a multihead microscope.

Towards Machine Consciousness: Grounding Abstract Models as pi-Processes

We present a two-level model of concurrent communicating systems (CCS) to serve as a basis formachine consciousness. A language implementing threads within logic programming is ¯rstintroduced. This high-level framework allows for the de¯nition of abstract processes that can beexecuted on a virtual machine. We then look for a possible grounding of these processes into thebrain. Towards this end, we map abstract de¯nitions (including logical expressions representingcompiled knowledge) into a variant of the pi-calculus. We illustrate this approach through aseries of examples extending from a purely reactive behavior to patterns of consciousness.

Exploring the use of mobile communications in a retirement home in Toronto: Preliminary results

This paper describes preliminary results of a qualitative case study on mobile communication conducted in an elders¿ retirement home in Toronto (Ontario, Canada) in May 2012. This is part of an international research project on the relationship between mobile communications and older people.Secondary data at a Canadian level contextualizes the case study. We focus ondemographic characteristics and on adoption and use of information and communication technologies (ICTs) broken by age.Participants in the study (21 individuals) are between 75 and 98 years of age, thereforewe can consider that the gathered evidence refers to the ¿old¿ older. Mobile phoneusers in the sample describe very specific uses of the mobile phone, while non-usersreport not facing external pressures for adopting that technology. The main channel formediated communication is the landline; in consequences mobile phones ¿when used¿ constitute an extra layer of communication. Finally, when members of the personal network of the individuals live abroad they are more prone to use Internet and Skype. We are also able to find ex-users of both mobile telephony and computers/internet who stopped using these technologies because they did not find any use for them.