The family alliance assessment scales : steps toward validity and reliability of an observational assessment tool for early family interactions

We present the first steps in the validation of an observational tool for father-mother-infant interactions: the FAAS (Family Alliance Assessment Scales). Family-level variables are acknowledged as unique contributors to the understanding of the socio-affective development of the child, yet producing reliable assessments of family-level interactions poses a methodological challenge. There is, therefore, a clear need for a validated and clinically relevant tool. This validation study has been carried out on three samples: one non-referred sample, of families taking part in a study on the transition to parenthood (normative sample; n = 30), one referred for medically assisted procreation (infertility sample; n = 30) and one referred for a psychiatric condition in one parent (clinical sample; n = 15). Results show that the FAAS scales have (1) good inter-rater reliability and (2) good validity, as assessed through known-group validity by comparing the three samples and through concurrent validity by checking family interactions against parents' self-reported marital satisfaction.

Capitalism in the classroom : the impact of self-enhancement values and normative assessment on student motivation, goals and attitudes towards cheating

Abstract The neo-liberal capitalist ideology has come under heavy fire with anecdotal evidence indicating a link between these same values and unethical behavior. Academic institutions reflect social values and act as socializing agents for the young. Can this explain the high and increasing rates of cheating that currently prevail in education? Our first chapter examines the question of whether self-enhancement values of power and açhievement, the individual level equivalent of neo-liberal capitalist values, predict positive attitudes towards cheating. Furthermore, we explore the mediating role of motivational factors. Results of four studies reveal that self-enhancement value endorsement predicts the adoption of performance-approach goals, a relationship mediated by introjected regulation, namely desire for social approval and that self-enhancement value endorsement also predicts the condoning of cheating, a relationship mediated by performance-approach goal adoption. However, self-transcendence values prescribed by a normatively salient source have the potential to reduce the link between self-enhancement value endorsément and attitudes towards cheating. Normative assessment constitutes a key tool used by academic institutions to socialize young people to accept the competitive, meritocratic nature of a sociéty driven by a neo-liberal capitalist ideology. As such, the manifest function of grades is to motivate students to work hard and to buy into the competing ethos. Does normative assessment fulfill these functions? Our second chapter explores the reward-intrinsic motivation question in the context of grading, arguably a high-stakes reward. In two experiments, the relative capacity of graded high performance as compared to the task autonomy experienced in an ungraded task to predict post-task intrinsic motivation is assessed. Results show that whilst the graded task performance predicts post-task appreciation, it fails to predict ongoing motivation. However, perceived autonomy experienced in non-graded condition, predicts both post-task appreciation and ongoing motivation. Our third chapter asks whether normative assessment inspires the spirit of competition in students. Results of three experimental studies reveal that expectation of a grade for a task, compared to no grade, induces greater adoption of performance-avoidance, but not performance-approach, goals. Experiment 3 provides an explanatory mechanism for this, showing that reduced autonomous motivation experienced in previous graded tasks mediates the relationship between grading and adoption of performance avoidance goals in a subsequent task. The above results, when combined, provide evidence as to the deleterious effects of self enhancement values and the associated practice of normative assessment in school on student motivation, goals and ethics. We conclude by using value and motivation theory to explore solutions to this problem.

Use of a multidimensional assessment tool in a psychiatric adolescent care unit.

AIMS: Adolescent mental health problems require treatment and care that are adapted to their needs. To evaluate this issue, it was decided to implement a multidimensional instrument focused on a global approach to adolescent social and behavioural functioning, combined with the ICD-10 classification. METHODS: The combination of an assessment interview and a classification tool enabled the method to integrate the measurement of several domains of patient-based outcome rather than focus on the measurement of symptoms. A group of 68 adolescents from an inpatient unit were compared with 67 adolescents from the general population. RESULTS: Results suggest that adolescents from the care unit adopt significantly riskier behaviour compared with adolescents from the control group. As expected, the main problems identified refer to the psychological and familial areas. A cluster analysis was performed and provided three different profiles: a group with externalizing disorders and two groups with internalizing disorders. On the basis of a structured interview it was possible to obtain information in a systematic way about the adolescents' trajectory (delinquency, physical and sexual abuse, psychoactive substance use). CONCLUSION: It was shown that treatment and care should not focus exclusively on mental health symptoms, but also upon physical, psychological and social aspects of the adolescent. A global approach helps in the consideration of the multitude of factors which must be taken into account when working with people with serious mental health problems and may help to turn the care unit's activity more specifically towards the needs of these adolescents.

The spiritual distress assessment tool: an instrument to assess spiritual distress in hospitalised elderly persons.

BACKGROUND: Although spirituality is usually considered a positive resource for coping with illness, spiritual distress may have a negative influence on health outcomes. Tools are needed to identify spiritual distress in clinical practice and subsequently address identified needs. This study describes the first steps in the development of a clinically acceptable instrument to assess spiritual distress in hospitalized elderly patients. METHODS: A three-step process was used to develop the Spiritual Distress Assessment Tool (SDAT): 1) Conceptualisation by a multidisciplinary group of a model (Spiritual Needs Model) to define the different dimensions characterizing a patient's spirituality and their corresponding needs; 2) Operationalisation of the Spiritual Needs Model within geriatric hospital care leading to a set of questions (SDAT) investigating needs related to each of the defined dimensions; 3) Qualitative assessment of the instrument's acceptability and face validity in hospital chaplains. RESULTS: Four dimensions of spirituality (Meaning, Transcendence, Values, and Psychosocial Identity) and their corresponding needs were defined. A formalised assessment procedure to both identify and subsequently score unmet spiritual needs and spiritual distress was developed. Face validity and acceptability in clinical practice were confirmed by chaplains involved in the focus groups. CONCLUSIONS: The SDAT appears to be a clinically acceptable instrument to assess spiritual distress in elderly hospitalised persons. Studies are ongoing to investigate the psychometric properties of the instrument and to assess its potential to serve as a basis for integrating the spiritual dimension in the patient's plan of care.

Basic indicators for better governance in international sport (BIBGIS) : an assessment tool for international sport governing bodies

This working paper presents the Basic Indicators for Better Governance in International Sport (BIBGIS) as a tool to assess and measure the state of governance of international sport governing bodies. The working paper is organised as follows. We start by presenting different definitions of governance and some examples of principles of good governance in sport and critique them. We then introduce our approach which is based on a limited number of indicators divided among seven dimensions and apply it to the International Olympic Committee (IOC) and other international sport governing bodies. Although our approach can also be used to benchmark the governance of different sport organisations, we demonstrate that it faces limitations. We conclude with suggested next steps for future BIBGIS developments.

Informing the judgments of fingerprint analysts using quality metric and statistical assessment tools

The aim of this research was to evaluate how fingerprint analysts would incorporate information from newly developed tools into their decision making processes. Specifically, we assessed effects using the following: (1) a quality tool to aid in the assessment of the clarity of the friction ridge details, (2) a statistical tool to provide likelihood ratios representing the strength of the corresponding features between compared fingerprints, and (3) consensus information from a group of trained fingerprint experts. The measured variables for the effect on examiner performance were the accuracy and reproducibility of the conclusions against the ground truth (including the impact on error rates) and the analyst accuracy and variation for feature selection and comparison.¦The results showed that participants using the consensus information from other fingerprint experts demonstrated more consistency and accuracy in minutiae selection. They also demonstrated higher accuracy, sensitivity, and specificity in the decisions reported. The quality tool also affected minutiae selection (which, in turn, had limited influence on the reported decisions); the statistical tool did not appear to influence the reported decisions.

The Prenatal Lausanne Trilogue Play: A New Observational Assessment Tool of the Prenatal Co-Parenting Alliance

The goal of this study is to present a new observational assessment tool, the prenatal Lausanne Trilogue Play situation (LTP). Expectant parents were asked to role play their first meeting with their baby using a doll, and the videotaped interaction was subsequently coded. Scores were correlated with measures of the couples' marital satisfaction as well as the postnatal family alliance 3 months after the baby's birth. Results showed that the prenatal co-parenting alliance was positively linked to both fathers' marital satisfaction as well as to the postnatal family alliance at 3 months. Thus, the prenatal LTP allows for assessment of the prenatal co-parenting alliance at the interactional level. It predicts the place the parents will afford their baby after birth and can contribute to methods of clinical assessment and prevention.

Incline, speed, and distance assessment during unconstrained walking.

Body accelerations during human walking were recorded by a portable measuring device. A new method for parameterizing body accelerations and finding the pattern of walking is outlined. Two neural networks were designed to recognize each pattern and estimate the speed and incline of walking. Six subjects performed treadmill walking followed by self-paced walking on an outdoor test circuit involving roads of various inclines. The neural networks were first "trained" by known patterns of treadmill walking. Then the inclines, the speeds, and the distance covered during overground walking (outdoor circuit) were estimated. The results show a good agreement between actual and predicted variables. The standard deviation of estimated incline was less than 2.6% and the maximum of the coefficient of variation of speed estimation is 6%. To the best of our knowledge, these results constitute the first assessment of speed, incline and distance covered during level and slope walking and offer investigators a new tool for assessing levels of outdoor physical activity.

Randomized trial of a computerized coronary heart disease risk assessment tool in HIV-infected patients receiving combination antiretroviral therapy.

BACKGROUND: Exposure to combination antiretroviral therapy (cART) can lead to important metabolic changes and increased risk of coronary heart disease (CHD). Computerized clinical decision support systems have been advocated to improve the management of patients at risk for CHD but it is unclear whether such systems reduce patients' risk for CHD. METHODS: We conducted a cluster trial within the Swiss HIV Cohort Study (SHCS) of HIV-infected patients, aged 18 years or older, not pregnant and receiving cART for >3 months. We randomized 165 physicians to either guidelines for CHD risk factor management alone or guidelines plus CHD risk profiles. Risk profiles included the Framingham risk score, CHD drug prescriptions and CHD events based on biannual assessments, and were continuously updated by the SHCS data centre and integrated into patient charts by study nurses. Outcome measures were total cholesterol, systolic and diastolic blood pressure and Framingham risk score. RESULTS: A total of 3,266 patients (80% of those eligible) had a final assessment of the primary outcome at least 12 months after the start of the trial. Mean (95% confidence interval) patient differences where physicians received CHD risk profiles and guidelines, rather than guidelines alone, were total cholesterol -0.02 mmol/l (-0.09-0.06), systolic blood pressure -0.4 mmHg (-1.6-0.8), diastolic blood pressure -0.4 mmHg (-1.5-0.7) and Framingham 10-year risk score -0.2% (-0.5-0.1). CONCLUSIONS: Systemic computerized routine provision of CHD risk profiles in addition to guidelines does not significantly improve risk factors for CHD in patients on cART.

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.

Validation of the spiritual distress assessment tool in older hospitalized patients.

ABSTRACT:¦BACKGROUND: The Spiritual Distress Assessment Tool (SDAT) is a 5-item instrument developed to assess unmet spiritual needs in hospitalized elderly patients and to determine the presence of spiritual distress. The objective of this study was to investigate the SDAT psychometric properties.¦METHODS: This cross-sectional study was performed in a Geriatric Rehabilitation Unit. Patients (N = 203), aged 65 years and over with Mini Mental State Exam score ≥ 20, were consecutively enrolled over a 6-month period. Data on health, functional, cognitive, affective and spiritual status were collected upon admission. Interviews using the SDAT (score from 0 to 15, higher scores indicating higher distress) were conducted by a trained chaplain. Factor analysis, measures of internal consistency (inter-item and item-to-total correlations, Cronbach α), and reliability (intra-rater and inter-rater) were performed. Criterion-related validity was assessed using the Functional Assessment of Chronic Illness Therapy-Spiritual well-being (FACIT-Sp) and the question "Are you at peace?" as criterion-standard. Concurrent and predictive validity were assessed using the Geriatric Depression Scale (GDS), occurrence of a family meeting, hospital length of stay (LOS) and destination at discharge.¦RESULTS: SDAT scores ranged from 1 to 11 (mean 5.6 ± 2.4). Overall, 65.0% (132/203) of the patients reported some spiritual distress on SDAT total score and 22.2% (45/203) reported at least one severe unmet spiritual need. A two-factor solution explained 60% of the variance. Inter-item correlations ranged from 0.11 to 0.41 (eight out of ten with P < 0.05). Item-to-total correlations ranged from 0.57 to 0.66 (all P < 0.001). Cronbach α was acceptable (0.60). Intra-rater and inter-rater reliabilities were high (Intraclass Correlation Coefficients ranging from 0.87 to 0.96). SDAT correlated significantly with the FACIT-Sp, "Are you at peace?", GDS (Rho -0.45, -0.33, and 0.43, respectively, all P < .001), and LOS (Rho 0.15, P = .03). Compared with patients showing no severely unmet spiritual need, patients with at least one severe unmet spiritual need had higher odds of occurrence of a family meeting (adjOR 4.7, 95%CI 1.4-16.3, P = .02) and were more often discharged to a nursing home (13.3% vs 3.8%; P = .027).¦CONCLUSIONS: SDAT has acceptable psychometrics properties and appears to be a valid and reliable instrument to assess spiritual distress in elderly hospitalized patients.

Mercury analysis in hair: comparability and quality assessment within the transnational COPHES/DEMOCOPHES project

Human biomonitoring (HBM) is an effective tool for assessing actual exposure to chemicals that takes into account all routes of intake. Although hair analysis is considered to be an optimal biomarker for assessing mercury exposure, the lack of harmonization as regards sampling and analytical procedures has often limited the comparison of data at national and international level. The European-funded projects COPHES and DEMOCOPHES developed and tested a harmonized European approach to Human Biomonitoring in response to the European Environment and Health Action Plan. Herein we describe the quality assurance program (QAP) for assessing mercury levels in hair samples from more than 1800 mother-child pairs recruited in 17 European countries. To ensure the comparability of the results, standard operating procedures (SOPs) for sampling and for mercury analysis were drafted and distributed to participating laboratories. Training sessions were organized for field workers and four external quality-assessment exercises (ICI/EQUAS), followed by the corresponding web conferences, were organized between March 2011 and February 2012. ICI/EQUAS used native hair samples at two mercury concentration ranges (0.20-0.71 and 0.80-1.63) per exercise. The results revealed relative standard deviations of 7.87-13.55% and 4.04-11.31% for the low and high mercury concentration ranges, respectively. A total of 16 out of 18 participating laboratories the QAP requirements and were allowed to analyze samples from the DEMOCOPHES pilot study. Web conferences after each ICI/EQUAS revealed this to be a new and effective tool for improving analytical performance and increasing capacity building. The procedure developed and tested in COPHES/DEMOCOPHES would be optimal for application on a global scale as regards implementation of the Minamata Convention on Mercury.

Development and preclinical assessment of a bioartificial pancreas.

Transplantation of insulin secreting cells is regarded as a possible treatment for type 1 diabetes. One major difficulty in this approach is, however, that the transplanted cells are exposed to the patient's inflammatory and autoimmune environment, which originally destroyed their own beta-cells. Therefore, even if a good source of insulin-secreting cells can be identified for transplantation therapy, these cells need to be protected against these destructive influences. The aim of this project was to evaluate, using a clonal mouse beta-cell line, whether genetic engineering of protective genes could be a viable option to allow these cells to survive when transplanted into autoimmune diabetic mice. We demonstrated that transfer of the Bcl-2 anti-apoptotic gene and of several genes specifically interfering with cytokines intracellular signalling pathways, greatly improved resistance of the cells to inflammatory stresses in vitro. We further showed that these modifications did not interfere with the capacity of these cells to correct hyperglycaemia for several months in syngeneic or allogeneic streptozocin-diabetic mice. However, these cells were not protected against autoimmune destruction when transplanted into type 1 diabetic NOD mice. This suggests that in addition to inflammatory attacks by cytokines, autoimmunity very efficiently kills the transplanted cells, indicating that multiple protective mechanisms are required for efficient transplantation of insulin-secreting cells to treat type 1 diabetes.