What young people with spina bifida want to know about sex and are not being told.

OBJECTIVE: The aim of this paper was to examine sexual knowledge, concerns and needs of youth with spina bifida (SB) to inform the medical community on ways to better support their sexual health. METHODS: As part of the Video Intervention/Prevention Assessment (VIA) - transitions, a prospective cohort study, 309 h of video data were collected from 14 participants (13-28 years old) with SB. Participants were loaned a video camcorder for 8-12 weeks to shoot visual narratives about any aspects of their lives. V/A visual narratives were analysed with grounded theory using NVivo. RESULTS: Out of 14 participants, 11 (six women) addressed issues surrounding romantic relationships and sexuality in their video clips. Analysis revealed shared concerns, questions and challenges regarding sexuality gathered under four main themes: romantic relationships, sexuality, fertility and parenthood, and need for more talk on sexuality. CONCLUSIONS: Youth with SB reported difficulties in finding answers to questions regarding their sexuality, romantic relationships and fertility. This study revealed a need for help from the medical community to inform and empower youth with SB in the area of sexual health. Through sexual and reproductive health education with patients and parents starting at an early age, medical providers can further encourage healthy emotional and physical development in adolescents transitioning into adulthood.

Women's decision making and experience of subsequent pregnancy following stillbirth.

INTRODUCTION: This study sought to increase understanding of women's thoughts and feelings about decision making and the experience of subsequent pregnancy following stillbirth (intrauterine death after 24 weeks' gestation). METHODS: Eleven women were interviewed, 8 of whom were pregnant at the time of the interview. Modified grounded theory was used to guide the research methodology and to analyze the data. RESULTS: A model was developed to illustrate women's experiences of decision making in relation to subsequent pregnancy and of subsequent pregnancy itself. DISCUSSION: The results of the current study have significant implications for women who have experienced stillbirth and the health professionals who work with them. Based on the model, women may find it helpful to discuss their beliefs in relation to healing and health professionals to provide support with this in mind. Women and their partners may also benefit from explanations and support about the potentially conflicting emotions they may experience during this time.

Mothers' and Fathers' Early Relationship With Their Infant: Similar Yet Temporally Discordant Themes [La relation initiale des parents avec leur nourrisson: Thèmes similaires quoique temporellement discordants]

Objective: To examine first-time mothers' and fathers' themes in their relationship with their infant, how these themes change during the first four months postpartum, and similarities and differences in mothers' and fathers' themes. Participants: Eighteen first-time mother-father couples were separately interviewed at one; six and 16 weeks postpartum. Data Analysis: Audio-taped, transcribed interviews were analysed using a Grounded Theory approach. Results: Our findings reveal a common set of themes for mothers and fathers in relation to the infant : 1: Discovery, 2: Physical Proximity, 3: Emotional Closeness, 4: Initiation of Complementary Interactions and 5: Commitment to Love and Care. However, there was a striking lack of concordance between mothers and fathers for these themes at each point in time. Conclusions: Mothers' and fathers' experience of the early relationship with their infant is unique. Focussing on maternal as well as paternal ways of experiencing the early relationship with their infant sets the way to understanding early developing relationships in the family context.

Le devenir féminin transgenre : une étude qualitative et réflexive sur le genre, la corporéité et la subjectivité sexuelle

RésuméCette thèse en psychologie qualitative et critique de la santé propose un éclairage, sur la subjectivité transgenre, différent des modèles dominants en clinique. Les nosologies de type DSM et de la psychiatrie dominante focalisent sur la seule question de la transition transsexuelle, elles utilisent la sexualité comme outil dans les diagnostics différentiels permettant d'effectuer le gatekeeping de la transition médicalisée du genre. Elles sont décrites comme un dispositif de médicalisation du genre, induisant des pratiques maltraitantes. Une méthodologie qualitative inspirée de la théorie ancrée ainsi que de l'analyse réflexive est utilisée. Un échantillon de 15 personnes représentant la diversité des personnes transgenres FtM a été recruté. Les données provenant d'entrevues non directives sont analysées dans une perspective verticale et horizontale. Les résultats soulignent l'inadéquation des typologies cliniques, de la place qui est donnée à la sexualité dans les procédures diagnostiques et de l'opposition qu'elles construisent entre identité (de genre) et sexualité. Ils plaident pour une vision deleuzienne de type nomade, incarnée et sexuée de la subjectivité transgenre.AbstractThe broad of this study in critical health psychology is to build an understanding of transgender subjectivity which contrast with dominant clinical models. DSM nosology types and dominant psychiatry have traditionally focused only on transsexual transitioning. They use sexuality as a diagnostic tool to address the gatekeeping of the medical transition. These practices have been described as medicalization of gender, inducing mistreatment. A qualitative methodology mixing grounded theory and reflexivity has been used. A sample of 15 persons has been recruited to represent transgender FtM diversity. Data were collected through in-depth interview and analysed case by case and by themes. Results show that dominant clinical typologies of TG are inappropriate, as well as the way sexuality is used in this practices and the opposition between (gender) identity and sexuality. We propose a deleuzian concept of becoming and multiplicity to understand transgender subjectivity.

Investigação qualitativa e abordagem psico-cultural do desvio [Qualitative research and psycho-cultural approaches of deviance]

Qualitative research and psycho-cultural approaches to deviant behaviour¦In this paper, the authors discuss the relevance of some historical, theoretical and¦methodological features of qualitative research for a psycho-cultural approach to¦deviance. Specifically, three methods are presented: ethnography, life stories and¦grounded theory. Some common features of these methods are: their potentialities of¦articulation with other methods, their plasticity and their procedures grounded in¦research contexts, experiences and meanings lived by participants. The role of the¦researcher, as well as the constructed and dialogical characteristics of both process¦and products of research, are also emphasised in these approaches. In this way,¦qualitative methods seem particularly adequate to a psycho-cultural approach to¦deviance, allowing the research of "hidden" phenomena and an understanding of¦deviance that takes into account its cultural norms. Thus, qualitative research is as a¦methodological device which allows to get beyond the traditional ethnocentrism of psychology.

Instruments for investigating fitness to drive - needs and expectations in primary care: a qualitative study

Background: Primary care physicians are often requested to assess their patients' fitness to drive. Little is however known on their needs to help them in this task. Aims: The aim of this study is to develop theories on needs, expectations, and barriers for clinical instruments helping physicians assess fitness to drive in primary care. Methods: This qualitative study used semi-structured interviews to investigate needs and expectations for instruments used to assess fitness to drive. From August 2011 to April 2013, we recorded opinions from five experts in traffic medicine, five primary care physicians, and five senior drivers. All interviews were integrally transcribed. Two independent researchers extracted, coded, and stratified categories relying on multi-grounded theory. All participants validated the final scheme. Results: Our theory suggests that for an instruments assessing fitness to drive to be implemented in primary care, it need to contribute to the decisional process. This requires at least five conditions: 1) it needs to reduce the range of uncertainty, 2) it needs to be adapted to local resources and possibilities, 3) it needs to be accepted by patients, 4) choices of tasks need to adaptable to clinical conditions, 5) and interpretation of results need to remain dependant of each patient's context. Discussion and conclusions: Most existing instruments assessing fitness to drive are not designed for primary care settings. Future instruments should also aim to support patient-centred dialogue, help anticipate driving cessation, and offer patients the opportunity to freely take their own decision on driving cessation as often as possible.

An innovative and shared methodology for event reconstruction using images in forensic science

This study presents an innovative methodology for forensic science image analysis for event reconstruction. The methodology is based on experiences from real cases. It provides real added value to technical guidelines such as standard operating procedures (SOPs) and enriches the community of practices at stake in this field. This bottom-up solution outlines the many facets of analysis and the complexity of the decision-making process. Additionally, the methodology provides a backbone for articulating more detailed and technical procedures and SOPs. It emerged from a grounded theory approach; data from individual and collective interviews with eight Swiss and nine European forensic image analysis experts were collected and interpreted in a continuous, circular and reflexive manner. Throughout the process of conducting interviews and panel discussions, similarities and discrepancies were discussed in detail to provide a comprehensive picture of practices and points of view and to ultimately formalise shared know-how. Our contribution sheds light on the complexity of the choices, actions and interactions along the path of data collection and analysis, enhancing both the researchers' and participants' reflexivity.

A thematic analysis of delusion with religious content in schizophrenia: open, closed, and mixed dynamics

The aim of the present study was to elicit how patients with delusions with religious contents conceptualized or experienced their spirituality and religiousness. Sixty-two patients with present or past religious delusions went through semistructured interviews, which were analyzed using the three coding steps described in the grounded theory. Three major themes were found in religious delusions: ''spiritual identity,'' ''meaning of illness,'' and ''spiritual figures.'' One higher-order concept was found: ''structure of beliefs.'' We identified dynamics that put these personal beliefs into a constant reconstruction through interaction with the world and others (i.e., open dynamics) and conversely structural dynamics that created a complete rupture with the surrounding world and others (i.e., closed structural dynamics); those dynamics may coexist. These analyses may help to identify psychological functions of delusions with religious content and, therefore, to better conceptualize interventions when dealing with it in psychotherapy.

A qualitative study of adolescents with medically unexplained symptoms and their parents. Part 2: How is healthcare perceived?

Medically unexplained symptoms (MUS) are common among adolescents and an important cause of clinical visits. This study sought to understand the experiences with, and perceptions of, the healthcare of adolescents who have MUS and their parents. Using a qualitative approach, six focus groups and two individual interviews were conducted with a total of ten adolescents and sixteen parents. The participants were recruited in a university hospital in Switzerland. A thematic analysis was conducted in accordance with the Grounded Theory. Six main themes emerged: needing a label for the symptoms, seeking an etiology to explain the symptoms, negotiating the medical system, medication and treatments, interactions with doctors, and the inclusion of parents during consultations. Transcending these themes, however, was the need for good communication between the adolescents, their parents and the clinicians. When explaining the symptoms, clinicians should make sure to discuss the results, investigations and lack of organic origin.

A qualitative study of adolescents with medically unexplained symptoms and their parents. Part 1: Experiences and impact on daily life.

Medically unexplained symptoms (MUS) are common among adolescents and are frequently encountered in primary care. Our aim was to explore how these adolescents and their parents experience the condition and its impact on their daily lives and to provide recommendations for health professionals. Using a qualitative approach, six focus groups and two individual interviews were conducted. These involved a total of ten adolescents with different types of MUS and sixteen parents. The respondents were recruited in a university hospital in Switzerland. A thematic analysis was conducted according to the Grounded Theory. The analysis of the data highlighted four core themes: disbelief, being different, concealing symptoms, and priority to adolescent's health. Transcending these themes was a core issue regarding the discrepancy between the strategies that adolescents and their parents use to cope with the symptoms. Health professionals should be made aware of the emotional needs of these patients and their families.

A qualitative study of adolescents with medically unexplained symptoms and their parents

Il est fréquent en médecine de premier recours de rencontrer des adolescents exprimant des symptômes somatiques pour lesquels aucune des investigations entreprises n'a permis de rendre compte d'une pathologie organique. De tels symptômes sont retrouvés dans la littérature sous la terminologie de symptômes médicalement inexpliqués (MUS) ou des troubles fonctionnels. Bien que la prévalence des adolescents souffrant de MUS est fréquente, les médecins éprouvent encore beaucoup de difficultés à prendre en charge et communiquer avec ces patients, principalement en raison d'une incompréhension de leurs besoins et préoccupations tant dans leur vie quotidienne que lors d'une consultation au cabinet. Le but de notre étude est de comprendre les expériences et vécus des adolescents avec des MUS ainsi que de leurs parents afin d'aider le praticien dans la compréhension de son patient dans sa globalité et ainsi d'améliorer sa prise en charge. Dans le premier article présenté, nous nous sommes intéressés à la vie quotidienne de ces adolescents en étudiant leurs relations avec leur famille et leur entourage ainsi que les répercussions sur leurs parcours scolaire et leurs activités extrascolaires. Dans le second article nous nous sommes penchés sur les relations qu'entretiennent ces adolescents et leurs parents avec le système de santé. Nous avons collecté des données qualitatives en moyennant des groupes focus incluant 16 adolescents atteints de troubles fonctionnels et leurs parents. L'analyse a permis de faire émerger les difficultés que ces jeunes et leurs familles vivent au quotidien et comment ils sont confrontés à la solitude dû principalement à l'incompréhension sociale. Les résultats mettent aussi en évidence l'insatisfaction de ces jeunes et de leurs parents par rapport à la prise en charge médical, notamment en raison d'un manque de communication. -- Medically unexplained symptoms (MUS) are common among adolescents and are frequently encountered in primary care. Our aim was to explore how these adolescents and their parents experience the condition and its impact on their daily lives and to provide recommendations for health professionals. Using a qualitative approach, six focus groups and two individual interviews were conducted. These involved a total of ten adolescents with different types of MUS and sixteen parents. The respondents were recruited in a university hospital in Switzerland. A thematic analysis was conducted according to the Grounded Theory. The analysis of the data highlighted four core themes: disbelief, being different, concealing symptoms, and priority to adolescent's health. Transcending these themes was a core issue regarding the discrepancy between the strategies that adolescents and their parents use to cope with the symptoms. Health professionals should be made aware of the emotional needs of these patients and their families.

Revising the Cannabis Use Disorders Identification Test (CUDIT) by means of Item Response Theory.

Cannabis use among adolescents and young adults has become a major public health challenge. Several European countries are currently developing short screening instruments to identify 'problematic' forms of cannabis use in general population surveys. One such instrument is the Cannabis Use Disorders Identification Test (CUDIT), a 10-item questionnaire based on the Alcohol Use Disorders Identification Test. Previous research found that some CUDIT items did not perform well psychometrically. In the interests of improving the psychometric properties of the CUDIT, this study replaces the poorly performing items with new items that specifically address cannabis use. Analyses are based on a sub-sample of 558 recent cannabis users from a representative population sample of 5722 individuals (aged 13-32) who were surveyed in the 2007 Swiss Cannabis Monitoring Study. Four new items were added to the original CUDIT. Psychometric properties of all 14 items, as well as the dimensionality of the supplemented CUDIT were then examined using Item Response Theory. Results indicate the unidimensionality of CUDIT and an improvement in its psychometric performance when three original items (usual hours being stoned; injuries; guilt) are replaced by new ones (motives for using cannabis; missing out leisure time activities; difficulties at work/school). However, improvements were limited to cannabis users with a high problem score. For epidemiological purposes, any further revision of CUDIT should therefore include a greater number of 'easier' items.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

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.

MyHits: a new interactive resource for protein annotation and domain identification.

The MyHits web server (http://myhits.isb-sib.ch) is a new integrated service dedicated to the annotation of protein sequences and to the analysis of their domains and signatures. Guest users can use the system anonymously, with full access to (i) standard bioinformatics programs (e.g. PSI-BLAST, ClustalW, T-Coffee, Jalview); (ii) a large number of protein sequence databases, including standard (Swiss-Prot, TrEMBL) and locally developed databases (splice variants); (iii) databases of protein motifs (Prosite, Interpro); (iv) a precomputed list of matches ('hits') between the sequence and motif databases. All databases are updated on a weekly basis and the hit list is kept up to date incrementally. The MyHits server also includes a new collection of tools to generate graphical representations of pairwise and multiple sequence alignments including their annotated features. Free registration enables users to upload their own sequences and motifs to private databases. These are then made available through the same web interface and the same set of analytical tools. Registered users can manage their own sequences and annotations using only web tools and freeze their data in their private database for publication purposes.