Maria Gabriella Grassia

Development and Validation of the Quality-of-Life Adolescent Cleft Questionnaire in Patients With Cleft Lip and Palate

Abstract Only a few reports in the literature have described the use of specific instruments for assessing the quality of life in adolescents and young adults with cleft lip and palate (CLP). This condition markedly affects their lifestyle, even after surgical treatment. In the present study, we aimed to develop a quality-of-life assessment tool specifically designed for such patients with CLP. Our multidisciplinary team created a questionnaire focused on the physical, psychological, and social satisfaction of adolescents and young adults with CLP, which was adapted from 3 dimensions of the 36-item Short-Form Health Survey. The questionnaire was administered to a randomized sample of 40 adolescents and young adults (aged 16–24 years) with CLP who had completed treatment protocols and 40 (aged 16–24 years) who were not affected by CLP. The statistical results stated that the questionnaire had good reliability and validity; the Cronbach &agr; coefficient was found to be 0.944. Moreover, factorial analysis confirmed the presence of 3 subscales that were the fundamental components of this questionnaire, which is consistent with the areas theoretically proposed and from which the items were designed and selected. Thus, we validated our novel questionnaire that was administered in the present study and proved its consistency. However, further investigations on a larger population would be useful to confirm these findings.

#Theterrormood: Studying the World Mood After the Terror Attacks on Paris and Bruxelles

The use of social media has become an increasingly significant phenomenon in contemporary society due to the huge and rapid advances in information technology. People are using social media on a daily basis to communicate their opinions with each other about a wide variety of subjects and general events. Social media communications include Facebook, Twitter, and many others. Twitter is one of the most widely used social media sites and has become an important tool for the assessment of public opinion on various different issues. Recently, several approaches for the evaluation of Twitter messages have been developed, identifying the relationships between words and sentiments associated with relevant keywords or hashtags. In this work, through Twitter, we examine people’s reactions to two tragic international events, namely the Paris and Bruxelles terror attacks. Specifically, we have collected comments on Twitter of users from various countries after the attacks. The data were collected using the “twitteR” package in the R programming language; all tweets that contained hashtags such as #notinmyname, #Paris, #PrayForParis, #PrayForTheWorld, #PrayForFrance, and #JeSuisParis from November 27 to December 4, 2015, and all tweets that contained hashtags such as #notinmyname, #PrayForBruxelles, #PrayForBelgium, #Bruxelles, and #PrayForTheWorld from April 5 to 13, 2016, were considered. The textual information was analyzed through techniques of text mining and network analysis in order to detect some important structures of people’s communications, so understanding their mood from these threads. Using some R packages, the data were cleaned and analyzed, to classify the tweets into different types of emotion.

Sparsity Data Reduction in Textual Network Analysis

In this paper, we propose a new strategy to derive an unweighted adjacency matrix from an affiliation matrix. The strategy is based on the use of a biclustering technique in order to reduce the sparsity of the matrix without changing the network structure. As an example, we implemented this approach to seek the common meaning of the term sustainability by using an affiliation matrix characterized by a core–periphery structure. The application of BiMax biclustering algorithm shows a sparsity reduction of the unweighted adjacency matrix with an invariant network structure.

Individual Disadvantage and Training Policies: The Construction of “Model-Based” Composite Indicators

In evaluating a policy, it is fundamental to represent its multiple dimensions and the targets it affects. Indeed, the impact of a policy generally involves a combination of socio-economic aspects that are difficult to represent. In this study, regional training policies are addressed, which are aimed at closing the huge gaps in employability and social inclusion of Italian trainees. Previous counterfactual estimates of the net impact of regional training policies reveal the need to observe and take into account the manifold aspects of trainees’ weaknesses. In fact, the target population consists of very disadvantaged individuals, who tend to experience difficult situations in the labour market. To overcome this shortfall, the present paper proposes Structural Equation Modelling (SEM) that considers the impact of trainees’ socio-economic conditions on the policy outcome itself. In particular, the ex ante human capital (HC) is estimated from the educational, social and individual backgrounds. Next, the labour and training policies augment the individual HC, affecting labour market outcomes jointly with individual job-search behaviour. All these phenomena are expressed by a wide set of manifest variables and synthesised by composite indicators calculated with Partial Least Squares SEM (SEM-PLS). The construction of the SEM is appraised and applied to the case of trainees in compulsory education.