Social trust : a public good?

Combining theories on social trust and social capital with sociopsychological approaches and applying contextual analyses to Swiss and European survey data, this thesis examines under what circumstances generalised trust, often understood as public good, may not benefit everyone, but instead amplify inequality. The empirical investigation focuses on the Swiss context, but considers different scales of analysis. Two broader questions are addressed. First, might generalised trust imply more or less narrow visions of community and solidarity in different contexts? Applying nonlinear principal component analysis to aggregate indicators, Study 1 explores inclusive and exclusive types of social capital in Europe, measured as regional configurations of generalised trust, civic participation and attitudes towards diversity. Study 2 employs multilevel models to examine how generalised trust, as an individual predisposition and an aggregate climate at the level of Swiss cantons, is linked to equality- directed collective action intention versus radical right support. Second, might high-trust climates impact negatively on disadvantaged members of society, precisely because they reflect a normative discourse of social harmony that impedes recognition of inequality? Study 3 compares how climates of generalised trust at the level of Swiss micro-regions and subjective perceptions of neighbourhood cohesion moderate the negative relationship between socio-economic disadvantage and mental health. Overall, demonstrating beneficial, as well as counterintuitive effects of social trust, this thesis proposes a critical and contextualised approach to the sources and dynamics of social cohesion in democratic societies. -- Cette thèse combine des théories sur le capital social et la confiance sociale avec des approches psychosociales et s'appuie sur des analyses contextuelles de données d'enquêtes suisses et européennes, afin d'étudier dans quelles circonstances la confiance généralisée, souvent présentée comme un bien public, pourrait ne pas bénéficier à tout le monde, mais amplifier les inégalités. Les études empiriques, centrées sur le contexte suisse, intègrent différentes échelles d'analyse et investiguent deux questions principales. Premièrement, la confiance généralisée implique-t-elle des visions plus ou moins restrictives de la communauté et de la solidarité selon le contexte? Dans l'étude 1, une analyse à composantes principales non-linéaire sur des indicateurs agrégés permet d'explorer des types de capital social inclusif et exclusif en Europe, mesurés par des configurations régionales de confiance généralisée, de participation civique, et d'attitudes envers la diversité. L'étude 2 utilise des modèles multiniveaux afin d'analyser comment la confiance généralisée, en tant que prédisposition individuelle et climat agrégé au niveau des cantons suisses, est associée à l'intention de participer à des actions collectives en faveur de l'égalité ou, au contraire, à l'intention de voter pour la droite radicale. Deuxièmement, des climats de haute confiance peuvent-ils avoir un impact négatif sur des membres désavantagés de la société, précisément parce qu'ils reflètent un discours normatif d'harmonie sociale qui empêche la reconnaissance des inégalités? L'étude 3 analyse comment des climats de confiance au niveau des micro-régions suisses et la perception subjective de faire partie d'un environnement cohésif modèrent la relation négative entre le désavantage socio-économique et la santé mentale. En démontrant des effets bénéfiques mais aussi contre-intuitifs de la confiance sociale, cette thèse propose une approche critique et contextualisée des sources et dynamiques de la cohésion sociale dans les sociétés démocratiques.

Polymorphisms in DNA-repair genes in a cohort of prostate cancer patients from different areas in Spain: heterogeneity between populations as a confounding factor in association studies

Background: Differences in the distribution of genotypes between individuals of the same ethnicity are an important confounder factor commonly undervalued in typical association studies conducted in radiogenomics. Objective: To evaluate the genotypic distribution of SNPs in a wide set of Spanish prostate cancer patients for determine the homogeneity of the population and to disclose potential bias. Design, Setting, and Participants: A total of 601 prostate cancer patients from Andalusia, Basque Country, Canary and Catalonia were genotyped for 10 SNPs located in 6 different genes associated to DNA repair: XRCC1 (rs25487, rs25489, rs1799782), ERCC2 (rs13181), ERCC1 (rs11615), LIG4 (rs1805388, rs1805386), ATM (rs17503908, rs1800057) and P53 (rs1042522). The SNP genotyping was made in a Biotrove OpenArrayH NT Cycler. Outcome Measurements and Statistical Analysis: Comparisons of genotypic and allelic frequencies among populations, as well as haplotype analyses were determined using the web-based environment SNPator. Principal component analysis was made using the SnpMatrix and XSnpMatrix classes and methods implemented as an R package. Non-supervised hierarchical cluster of SNP was made using MultiExperiment Viewer. Results and Limitations: We observed that genotype distribution of 4 out 10 SNPs was statistically different among the studied populations, showing the greatest differences between Andalusia and Catalonia. These observations were confirmed in cluster analysis, principal component analysis and in the differential distribution of haplotypes among the populations. Because tumor characteristics have not been taken into account, it is possible that some polymorphisms may influence tumor characteristics in the same way that it may pose a risk factor for other disease characteristics. Conclusion: Differences in distribution of genotypes within different populations of the same ethnicity could be an important confounding factor responsible for the lack of validation of SNPs associated with radiation-induced toxicity, especially when extensive meta-analysis with subjects from different countries are carried out.

Lower incisor dentoalveolar compensation and symphysis dimensions among Class I and III malocclusion patients with different facial vertical skeletal patterns

Objective: To compare lower incisor dentoalveolar compensation and mandible symphysis morphology among Class I and Class III malocclusion patients with different facial vertical skeletal patterns. Materials and Methods: Lower incisor extrusion and inclination, as well as buccal (LA) and lingual (LP) cortex depth, and mandibular symphysis height (LH) were measured in 107 lateral cephalometric x-rays of adult patients without prior orthodontic treatment. In addition, malocclusion type (Class I or III) and facial vertical skeletal pattern were considered. Through a principal component analysis (PCA) related variables were reduced. Simple regression equation and multivariate analyses of variance were also used. Results: Incisor mandibular plane angle (P < .001) and extrusion (P  =  .03) values showed significant differences between the sagittal malocclusion groups. Variations in the mandibular plane have a negative correlation with LA (Class I P  =  .03 and Class III P  =  .01) and a positive correlation with LH (Class I P  =  .01 and Class III P  =  .02) in both groups. Within the Class III group, there was a negative correlation between the mandibular plane and LP (P  =  .02). PCA showed that the tendency toward a long face causes the symphysis to elongate and narrow. In Class III, alveolar narrowing is also found in normal faces. Conclusions: Vertical facial pattern is a significant factor in mandibular symphysis alveolar morphology and lower incisor positioning, both for Class I and Class III patients. Short-faced Class III patients have a widened alveolar bone. However, for long-faced and normal-faced Class III, natural compensation elongates the symphysis and influences lower incisor position.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

A statistical shape model of the human second cervical vertebra.

PURPOSE: Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. METHODS: The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. RESULTS: The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. CONCLUSION: The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Does phylogeographic structure relate to climatic niche divergence? A test using maritime pine (Pinus pinaster Ait.)

Aim To disentangle the effects of environmental and geographical processes driving phylogenetic distances among clades of maritime pine (Pinus pinaster). To assess the implications for conservation management of combining molecular information with species distribution models (SDMs; which predict species distribution based on known occurrence records and on environmental variables). Location Western Mediterranean Basin and European Atlantic coast. Methods We undertook two cluster analyses for eight genetically defined pine clades based on climatic niche and genetic similarities. We assessed niche similarity by means of a principal component analysis and Schoener's D metric. To calculate genetic similarity, we used the unweighted pair group method with arithmetic mean based on Nei's distance using 266 single nucleotide polymorphisms. We then assessed the contribution of environmental and geographical distances to phylogenetic distance by means of Mantel regression with variance partitioning. Finally, we compared the projection obtained from SDMs fitted from the species level (SDMsp) and composed from the eight clade-level models (SDMcm). Results Genetically and environmentally defined clusters were identical. Environmental and geographical distances explained 12.6% of the phylogenetic distance variation and, overall, geographical and environmental overlap among clades was low. Large differences were detected between SDMsp and SDMcm (57.75% of disagreement in the areas predicted as suitable). Main conclusions The genetic structure within the maritime pine subspecies complex is primarily a consequence of its demographic history, as seen by the high proportion of unexplained variation in phylogenetic distances. Nevertheless, our results highlight the contribution of local environmental adaptation in shaping the lower-order, phylogeographical distribution patterns and spatial genetic structure of maritime pine: (1) genetically and environmentally defined clusters are consistent, and (2) environment, rather than geography, explained a higher proportion of variation in phylogenetic distance. SDMs, key tools in conservation management, better characterize the fundamental niche of the species when they include molecular information.

Macrofossils in Raraku Lake (Easter Island) integrated with sedimentary and geochemical records towards a palaeoecological synthesis for the last 34,000 years

Macrofossil analysis of a composite 19 m long sediment core from Rano Raraku Lake (Easter Island)was related to litho-sedimentary and geochemical features of the sediment. Strong stratigraphical patterns are shown by indirect gradient analyses of the data. The good correspondence between the stratigraphical patterns derived from macrofossil (Correspondence Analysis) and sedimentary and geochemical data (Principal Component Analysis) shows that macrofossil associations provide sound palaeolimnological information in conjunction with sedimentary data. The main taphonomic factors in fluencing the macrofossil assemblages are run-off from the catchment, the littoral plant belt, and the depositional environment within the basin. Five main stages during the last 34,000 calibrated years BP (cal yr BP) are characterised from the lithological, geochemical, and macrofossil data. From 34 to 14.6 cal kyr BP (last glacial period) the sediments were largely derived from the catchment, indicating a high energy lake environment with much erosion and run-off bringing abundant plant trichomes, lichens, and mosses into the centre of Raraku Lake.

Measuring Intermediary Determinants of Early Childhood Health: A Composite Index Comparing Colombian Departments

In recent years there has been growing interest in composite indicators as an efficient tool of analysis and a method of prioritizing policies. This paper presents a composite index of intermediary determinants of child health using a multivariate statistical approach. The index shows how specific determinants of child health vary across Colombian departments (administrative subdivisions). We used data collected from the 2010 Colombian Demographic and Health Survey (DHS) for 32 departments and the capital city, Bogotá. Adapting the conceptual framework of Commission on Social Determinants of Health (CSDH), five dimensions related to child health are represented in the index: material circumstances, behavioural factors, psychosocial factors, biological factors and the health system. In order to generate the weight of the variables, and taking into account the discrete nature of the data, principal component analysis (PCA) using polychoric correlations was employed in constructing the index. From this method five principal components were selected. The index was estimated using a weighted average of the retained components. A hierarchical cluster analysis was also carried out. The results show that the biggest differences in intermediary determinants of child health are associated with health care before and during delivery.

Measuring early childhood health : a composite index comparing Colombian departments

This paper presents a composite index of early childhood health using a multivariate statistical approach. The index shows how child health varies across Colombian departments, -administrative subdivisions-. In recent years there has been growing interest in composite indicators as an efficient analysis tool and a way of prioritizing policies. These indicators not only enable multi-dimensional phenomena to be simplified but also make it easier to measure, visualize, monitor and compare a country’s performance in particular issues. We used data collected from the Colombian Demographic and Health Survey, DHS, for 32 departments and the capital city, Bogotá, in 2005 and 2010. The variables included in the index provide a measure of three dimensions related to child health: health status, health determinants and the health system. In order to generate the weight of the variables and take into account the discrete nature of the data, we employed a principal component analysis, PCA, using polychoric correlation. From this method, five principal components were selected. The index was estimated using a weighted average of the components retained. A hierarchical cluster analysis was also carried out. We observed that the departments ranking in the lowest positions are located on the Colombian periphery. They are departments with low per capita incomes and they present critical social indicators. The results suggest that the regional disparities in child health may be associated with differences in parental characteristics, household conditions and economic development levels, which makes clear the importance of context in the study of child health in Colombia.

Uma introdução à análise exploratória de dados multivariados

The modern technological ability to handle large amounts of information confronts the chemist with the necessity to re-evaluate the statistical tools he routinely uses. Multivariate statistics furnishes theoretical bases for analyzing systems involving large numbers of variables. The mathematical calculations required for these systems are no longer an obstacle due to the existence of statistical packages that furnish multivariate analysis options. Here basic concepts of two multivariate statistical techniques, principal component and hierarchical cluster analysis that have received broad acceptance for treating chemical data are discussed.

Metais pesados em águas superficiais como estratégia de caracterização de bacias hidrográficas

The input of heavy metals concentrations determinated by ICP-AES, in samples of the Cambé river basin, was evaluated by using the Principal Component Analysis. The results distinguishes clearly one site, which is strongly influenced by almost all elements studied. Special attention was given to Pb, because of the presence of one battery industry in this area. Some downstream samples were associated with the same characteristics of this site, showing residual action of contaminants along the basin. Other sites presented influence of soil elements, plus Cr near a tannery industry. This study allowed to distinguish different sites in the upper basin of the Cambé (Londrina-PR-BR), in accordance to elements input.

Spatial and Temporal Trends in Sunshine Duration over Western Europe

This work analyzes sunshine duration variability in the western part of Europe (WEU) over the 1938– 2004 period. A principal component analysis is applied to cluster the original series from 79 sites into 6 regions, and then annual and seasonal mean series are constructed on regional and also for the whole WEU scales. Over the entire period studied here, the linear trend of annual sunshine duration is found to be nonsignificant. However, annual sunshine duration shows an overall decrease since the 1950s until the early 1980s, followed by a subsequent recovery during the last two decades. This behavior is in good agreement with the dimming and brightening phenomena described in previous literature. From the seasonal analysis, the most remarkable result is the similarity between spring and annual series, although the spring series has a negative trend; and the clear significant increase found for the whole WEU winter series, being especially large since the 1970s. The behavior of the major synoptic patterns for two seasons is investigated, resulting in some indications that sunshine duration evolution may be partially explained by changes in the frequency of some of them

The Quality of Paediatric Nursing Care - Children’s Perspective

Hoitotyön laatu - lasten näkökulma Tämän kolmivaiheisen tutkimuksen tarkoituksena oli kuvailla lasten odotuksia ja arviointeja lasten hoitotyön laadusta sekä kehittää mittari kouluikäisille sairaalassa oleville lapsille laadun arviointiin. Perimmäisenä tavoitteena oli lasten hoitotyön laadun kehittäminen sairaalassa. Ensimmäisessä vaiheessa 20 alle kouluikäistä (4-6v) sekä 20 kouluikäistä (7-11v) lasta kuvailivat odotuksiaan lasten hoitotyön laadusta. Aineisto kerättiin haastattelulla ja lasten piirustusten avulla, sekä analysoitiin sisällön analyysilla. Lasten odotukset lasten hoitotyön laadusta kohdistuivat hoitajaan, hoitotyön toimintoihin ja ympäristöön, fyysinen ympäristö korostui piirustuksissa. Ensimmäisen vaiheen tulosten, aikaisemman kirjallisuuden sekä Leino-Kilven “HYVÄ HOITO” mittarin pohjalta kehitettiin “Lasten Hoidon Laatu Sairaalassa” (LHLS) mittari ja testattiin sen psykometrisiä ominaisuuksia tutkimuksen toisessa vaiheessa. Mittaria kehitettiin ja testattiin kolmen vaiheen kautta. Aluksi asiantuntijapaneeli (n=7) arvioi mittarin sisältöä. Seuraavaksi mittari esitestattiin kahdesti kouluikäisillä sairaalassa olevilla lapsilla (n=41 ja n=16), samassa vaiheessa myös viiden lastenosaston hoitajat (n=19) yhdessä arvioivat mittarin sisältöä sekä 8 lasta. Lopuksi mittaria testattiin kouluikäisillä lapsilla (n=388) sairaalassa sekä hoitajat (n=198) arvioivat mittarin sisällön validiteettia. Mittarin kehittämisen aikana päälaatuluokkien: hoitajan ominaisuudet, hoitotyön toiminnot ja hoitotyön ympäristö Cronbachin alfa kertoimet paranivat. Pääkomponentti analyysi tuki mittarin hoitotyön toimintojen ja ympäristön alaluokkien teoreettista rakennetta. Kolmannessa vaiheessa “Lasten Hoidon Laatu Sairaalassa” (LHLS III, versio neljä) mittarilla kerättiin aineisto Suomen yliopistosairaaloiden lastenosastoilta kouluikäisiltä 7-11 -vuotiailta lapsilta (n=388). Mittarin lopussa lapsia pyydettiin lisäksi kuvailemaan kivointa ja ikävintä kokemustaan sairaalahoidon aikana lauseen täydennystehtävänä. Aineisto analysoitiin tilastollisesti sekä sisällön analyysilla. Lapset arvioivat fyysisen hoitoympäristön, hoitajien inhimillisyyden ja luotettavuuden sekä huolenpidon ja vuorovaikutustoiminnot kiitettäviksi. Lapset arvioivat hoitajien viihdyttämistoiminnot kaikkein alhaisimmiksi. Lapsen ikä ja sairaalantulotapa olivat yhteydessä lasten saamaan tiedon määrään. Lasten kivoimmat kokemukset liittyivät ihmisiin ja heidän ominaisuuksiinsa, toimintoihin, ympäristöön sekä lopputuloksiin. Ikävimmät kokemukset liittyivät potilaana oloon, tuntemuksiin sairauden oireista sekä erossaoloon, hoitotyön fyysisiin toimintoihin sekä ympäristöön. Tutkimuksen tulokset osoittavat lasten olevan kykeneviä arvioimaan omaa hoitoaan ja heidän näkökulmansa tulisi nähdä osana koko laadun kehittämisprosessia parannettaessa laatua käytännössä todella lapsilähtöisemmällä lähestymistavalla. “Lasten Hoidon Laatu Sairaalassa” (LHLS) mittari on mahdollinen väline saada tietoa lasten arvioinneista lasten hoitotyön laadusta, mutta mittarin testaamista tulisi jatkaa tulevaisuudessa

Quimiometria I: calibração multivariada, um tutorial

The aim of this work is to present a tutorial on Multivariate Calibration, a tool which is nowadays necessary in basically most laboratories but very often misused. The basic concepts of preprocessing, principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) are given. The two basic steps on any calibration procedure: model building and validation are fully discussed. The concepts of cross validation (to determine the number of factors to be used in the model), leverage and studentized residuals (to detect outliers) for the validation step are given. The whole calibration procedure is illustrated using spectra recorded for ternary mixtures of 2,4,6 trinitrophenolate, 2,4 dinitrophenolate and 2,5 dinitrophenolate followed by the concentration prediction of these three chemical species during a diffusion experiment through a hydrophobic liquid membrane. MATLAB software is used for numerical calculations. Most of the commands for the analysis are provided in order to allow a non-specialist to follow step by step the analysis.

Avaliação do uso de métodos quimiométricos em análise de solos

One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.