Gaming for Healthcare: A Bibliometric Analysis in Business and Management

The purpose of this paper is to scrutinize and classify the literature linking gaming for healthcare and management phenomena. An objective bibliometric analysis is conducted, supported by subjective assessments based on studies focused on the linking of gaming for healthcare and management fields. From the analysis and its evaluation, three clusters depicting literature linking gaming for healthcare and management phenomena are showed: management and governance public/private healthcare system; gaming and knowledge/strategic management; management health/medical insurance system using game theory. Moreover, the study shows the limits of existing literature on this topic and proposes future research topics. This is one of the first attempts to comprehend the research stream which, over time, has paved the way to the intersection between gaming for healthcare and management fields

Objective Measurements of Student Satisfaction by Comparing the Effects of Different Factors

Item Response Theory (IRT) is proposed in literature for evaluating the customer satisfaction in private and public service. In this work a particular version of IRT, known as Partial Credit Model (PCM), is proposed to analyse the satisfaction of university students of a middle university of Southern Italy. Moreover, to consider the effects of different factors (degree course, departments, presence of integrative teaching activities, etc) and their interactions, factorial differential item functioning proposed for the Rasch model is extended to PCM

Objective Measurements of Student Satisfaction by Comparing the Effects of Different Factors

AbstractItem Response Theory (IRT) is proposed in literature for evaluating the customer satisfaction in private and public service. In this work a particular version of IRT, known as Partial Credit Model (PCM), is proposed to analyse the satisfaction of university students of a middle university of Southern Italy. Moreover, to consider the effects of different factors (degree course, departments, presence of integrative teaching activities, etc) and their interactions, factorial differential item functioning proposed for the Rasch model is extended to PCM

THE RASCH MODEL FOR EVALUATING ITALIAN STUDENT PERFORMANCE

In 1997 the Organisation for Economic Co-operation and Development (OECD) launched the OECD Programme for International Student Assessment (PISA) for collecting information about 15-year-old students in participating countries.Our study analyse the PISA 2006 cognitive test for evaluating the Italian student performance in mathematics, reading and science comparing the results of different local governments. For this purpose the most proper statistic methodology is Item Response Theory - IRT that collects several models, the simplest is Rasch Model – MR (1960). As the items used in the analysis are both dichotomous that polytomous, we apply Partial Credit Model (PCM)

Multinuclear solid state NMR of novel bioactive glass and nanocomposite tissue scaffolds

Sol-gel derived bioactive glasses are promising candidates for bone regeneration, where bone is a natural nanocomposite of collagen (organic polymer) and hydroxyapatite (inorganic mineral) with a complex hierarchical structure and excellent mechanical properties. Solid-state NMR is a sensitive probe and offers atomic-level information on the structure of sol-gel derived bioactive glasses. In this thesis, a multinuclear solid state NMR approach, as part of an extensive study, has been applied to a key range of sol-gel derived materials related to novel nanocomposites to act as tissue scaffolds. The nanostructure evolution of sol-gel derived bioactive glasses 70S30C (70 mol% SiO2 and 30 mol% CaO) was characterised by 29Si, 1H and 13C CP MAS NMR. Calcium was found to be incorporated into the silica network during the stabilisation stage and to increases its disorder. The inhomogeneity found within 70S30C bioactive glass monoliths showed that the calcium concentration was higher in the outer region of the monolith caused by the way calcium only enters into the structure after breakbown of the nitrate. Trimethylsilylation reaction mechanisms used to tailor the nanoporosity of sol-gel derived 70S30C bioactive glass was also studied. The 29Si NMR results showed that the modification processes affected the atomic scale structure of the glass, such as Qn structure and network connectivity. 1H and 13C NMR was used to follow the loss of hydroxyls and organic groups directly. The study was extended to 58S (60 mol% SiO2, 36 mol% CaO, 4 mol% P2O5) systems and compared for two synthesis routes: inorganic and alkoxide. Via the inorganic route high temperatures were needed for calcium incorporation, while via alkoxide route calcium was found to be incorporated at low temperatures. Reactive surface Ca ions were involved in the formation of different types of carbonates for the two routes. The addition of P2O5 to the silica-calcium oxide system results in a scavenging of calcium ions by phosphate groups to give orthophosphate and pyrophosphate units. Solid-state NMR of new organic-inorganic hybrid scaffolds, class II, in the silicagelatin and silica-calcium oxide-poly(γ-glutamic acid) (γ-PGA) systems indicates that 3- glycidoxypropyltrimethoxysilane (GPTMS) provides a covalent link between the organic and inorganic networks and increased the inorganic condensation. 1H-1H intra- and intermolecular proximities have been identified using 1H DQ (double-quantum) CRAMPS (combined rotation and multiple pulse spectroscopy) techniques. 13C NMR results indicate that an efficient promotion of epoxide ring opening of GPTMS was reached by either gelatin or γ-PGA. 43Ca NMR identified different calcium environments in the hybrid systems. The last part of this thesis is focused on the comparison studies in the mechanism of apatite growth on both melt-derived (Bioglass®) and sol-gel derived (TheraGlass®) bioactive glass surfaces. By using a combination of 1H, 13C, 31P, 29Si and 23Na, using one and two dimensional NMR spectroscopy, the inhibitive effects of serum proteins in the mechanism of the apatite growth was revealed. The solid-state NMR experimental data support the hydroxycarbonate apatite formation mechanism proposed by Hench. Apatite formation takes place from the largely amorphous phosphate ions initially deposited on the glass surface. Serum proteins adsorbed on the glass surface have been found to significantly inhibit the apatite formation. Multiple sodium sites have been identified in Bioglass® composition with the formation of a more ordered local structure on increasing immersion time.EThOS - Electronic Theses Online ServiceUniversity of WarwickEngineering and Physical Sciences Research Council (EPSRC)GBUnited Kingdo

Robust methods for Partial Least Squares Regression: methodological contributions and applications in environmental field

Several epidemiological studies demonstrated short-term associations between high levels of pollution and increased acute mortality and morbidity. Vehicles emissions are an important source of environmental pollution, so it’s necessary to estimate pollution emissions caused by classes of vehicles in different situations (traffic, road, etc) in order to reduce environmental pollution. The analysis is based on a research developed by the Italian National Research Council (CNR), concerning the relationship between the pollutants produced by auto vehicles and the kinematics parameters, considering different traffic and road situations (driving cycles). The model, based on the vehicle dynamic equation, shows variables strongly correlated, missing data and few observations, so the most proper statistic methodology to analyse the data with these characteristics is the Partial Least Squares (PLS) regression. The results of the CNR analysis showed as the different driving cycles (traffic, road, etc) can produce outliers, because of the different kinematics variables generated. The aim of this thesis is to analyse the proposed model taking into account the outliers by applying a robust approach to the PLS regression. We proceed in the following way. In the first chapter we show that the presence of multicollinearity between the independent variables in regression analysis yields Ordinary Least Squares (OLS) inapplicable, so we have to use other technique, like Ridge Regression, Principal Component Regression, Latent Root regression Analysis, Partial Least Squares (PLS) regression. It’s has been stated that in a lot of cases PLS is the better solution. However the results are affected by outliers. In the second chapter we describe the most important robust methods for estimating the regression parameters and variance/covariance matrix. Unfortunately several affine equivariant estimators with high breakdown point can not be applied when the number of units is smaller than the number of variables. Therefore we propose an approach which combines “leave-one-out” methods and Singular Value Decomposition (SVD). We call this method SSVD. In the third chapter we show that both the algorithms for PLS regression: NIPALS and SIMPLS are affected by outliers. SIMPLS algorithm’s sensitivity to outliers is due to use of cross-covariance matrix between independent and dependent variables as well as and the use of least squares regressions. The NIPALS algorithm’s sensitivity to outliers is due to use of least squares regressions. There are two ways to solve the problem of outliers. The first is to use regression diagnostic to detect outliers. For the multivariate nature of the data, it can be very difficult to detect outliers. The second is to use a robust procedure for PLS regression. Several procedures have been proposed, but evidence of their use in the statistical literature is still scarce. A first class of robust alternatives for PLS regression involves the application of robust regression to the NIPALS algorithm. A second class includes methods which use a robust cross-covariance matrix and a robust regression method. We describe the different robust alternatives, their advantages and disadvantages, propose a robust approach and end with a simulation study. In the fourth chapter we apply some robust methods for PLS regression and our approach on environmental data of CNR in order to compare the results and show that our approach is a valid alternative in presence of multicollinearity and outliers

Estimating multinomial logit model with multicollinear data

EnThe multinomial model is used to study the dependence relationship between a categorical response variable with more than two categories and a set of explicative variables. In presence of multicollinearity, the estimation of the multinomial model parameters becomes inaccurate. To solve this problem we develop an extension of Principal Component Logistic Regression (PCLR), model proposed by Aguilera et al. (2006). Finally a case study illustrates the advantages of the method

Correspondence Analysis for doubly cumulative contingency table.

A suitable measure of association for two ordered variables is the doubly cumulative chi-squared statistic (Hirotsu, 1994). This statistic is obtained by considering the cumulative sum of cell frequencies across the variables. In this paper we explore the development of correspondence analysis which takes into account the presence of two ordered variables by partitioning the doubly cumulative chi-squared statistic

Dealing with multicollinearity and outliers in multinomial logit model: a simulation study.

Multinomial logit model is a powerful tool for modeling the dependence relationship between a set of quantitative regressors and a categorical response variable with more than two categories. However, it necessitates careful consideration of multicollinearity among the regressors and examination for outliers. For dealing with these problems in the multinomial logit model, an approach based on robust principal components has been developed. The robust approach has achieved satisfactory results on real data. This paper aims to validate such an approach by a simulation study

A model for assessing sea environmental quality

In this paper, we aim to study the key factors which affect the environmental quality of the sea. The Fp-ratio, utilized as an indicator of the trophic state of the coastal waters, is classified into three categories. Given the ordinal nature of the response variable, we believe ordinal logistic regression models are the most proper statistic methodology. As the regressors are strongly correlated, the parameters of ordinal logistic regression models cannot be estimated. For solving this problem and, in general, the consequences of multicollinearity, we develop an approach based on principal components. The new approach will be applied for estimating the Fp-ratio