Poverty and Social Exclusion in the European Union: South-Eastern Territorial Patterns

Despite the ambitious goals of promoting inclusive growth in the Europe 2020 strategy, the number of people at risk of poverty in European Union is still growing. The paper moves from the hypothesis that poverty may show distinctive social patterns, which couple with a given spatial dimension and therefore can be defined as a spatially heterogeneous phenomenon at both national and sub-national level. Using the available data from Eurostat on income and living conditions (EUSILC) at NUTS2 level, the paper highlights the different territorial patterns in shaping the risk of exclusion across the EU regions. Focusing on the regions of the Southern and Eastern peripheral EU Member States (MSs), the paper outlines the differences emerging from the results achieved by the EU MSs in applying the Europe 2020 Strategy. Moreover, it deepens the analysis of the poverty drivers at the regional level. The paper shows that the peripheral countries of EU are more vulnerable to poverty but different patterns emerge when comparing Mediterranean and Eastern countries, especially with regard to the material deprivation and the drivers influencing poverty and risk of exclusion

R&I smart specialisation strategies: classification of EU regions’ priorities. Results from automatic text analysis

Building on automatic text analysis, this paper proposes an original categorization of Research and Innovation Smart Specialisation Strategy (RIS3) priorities and provides a common language (with detailed dictionaries) to classify priorities and then to associate EU regions to multiclass categories of priorities. This result is a powerful tool to interpret the current state of diversification across regions, with its potential of complementarities and synergies that might support territorial integrated development paths. It would also support regions in their future strategic programmes on RIS3. A case study on the Alpine macro-region shows innovation development paths to outline macroregion strategic planning

Multidimensional clustering of EU regions. A contribution to orient public policies in reducing regional disparities

This paper applies multidimensional clustering of EU-28 regions with regard to their specialisation strategies and socioeconomic characteristics. It builds on an original dataset. Several academic studies discuss the relevant issues to be addressed by innovation and regional development policies, but so far no systematic analysis has linked the different aspects of EU regions research and innovation strategies (RIS3) and their socio-economic characteristics. This paper intends to fill this gap, with the aim to provide clues for more effective regional and innovation policies. In the data set analysed in this paper, the socioeconomic and demographic classification associates each region to one categorical variable (with 19 categories), while the classification of the RIS3 priorities clustering was performed separately on “descriptions” (21 Boolean categories) and “codes” (11 Boolean Categories) of regions’ RIS3. The cluster analysis, implemented on the results of the correspondence analysis on the three sets of categories, returns 9 groups of regions that are similar in terms of priorities and socioeconomic characteristics. Each group has different characteristics that revolve mainly around the concepts of selectivity (group’s ability to represent a category) and homogeneity (similarity in the group with respect to one category) with respect to the different classifications on which the analysis is based. Policy implications showed in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and to the enhance complementarities and synergies within macro-regions

Detecting multidimensional clustering across EU regions. Focus on R&I smart specialisation strategies and on socio-economic and demographic conditions

This paper applies multidimensional clustering of EU-28 regions to identify similar specialisation strategies and socioeconomic characteristics. It builds on an original dataset where the EU-28 regions are classified according to their socioeconomic and demographic features and to the strategic priorities outlined in their research and innovation smart specialisations strategy (RIS3). The socioeconomic and demographic classification associates each region to one categorical variable (with 19 modalities), while the classification of the RIS3 priorities clustering was performed separately on “descriptions” (21 Boolean categories) and “codes” (11 Boolean Categories) of regions’ RIS3. Three techniques of clustering have been applied: Infomap multilayer algorithm, Correspondence Analysis plus Cluster Analysis and cross tabulation. The most effective clustering, in terms of both the characteristics of the data and the emerging results, is that obtained on the results of the Correspondence Analysis. By contrast, due to the very dense network induced by the data characteristics, the Infomap algorithm does not produce significant results. Finally, cross tabulation is the most detailed tool to identify groups of regions with similar characteristics. In particular, in the paper we present an application of cross tabulation to focus on the regions investing in sustainable development priorities. Policy implications of methods implemented in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and the enhancement of complementarities and synergies within macroregions

Incidence of traumatic spinal cord injury in Italy during 2013–2014: a population-based study

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Self-management in stroke survivors: Development and implementation of the look after yourself (lay) intervention

Objective: Self-management is recommended in stroke rehabilitation. This report aims to describe timing, contents, and setting of delivery of a patient-centered, self-management program for stroke survivors in their early hospital rehabilitation phase: the Look After Yourself (LAY) in-tervention. Methods: After extensive literature search, the LAY intervention was developed by in-tegrating the Chronic Disease Self-Management Program, based on the self-efficacy construct of social cognitive theory, with evidence-based key elements and input from stroke survivors. Results: the LAY intervention aims to implement self-management skills in stroke survivors, enabling them to be active in goal setting and problem solving using action plans and to facilitate the critical transition from hospital to community. It includes both group sessions to facilitate sharing of experi-ences, social comparison, vicarious learning, and increase motivation and one-to-one sessions fo-cused on setting feasible action plans and on teaching personalized strategies to prevent falls. Stand-ardization is ensured by manuals for facilitators and patients. Conclusion: The LAY intervention is the first Italian program to support early self-management in stroke rehabilitation; it has been ex-perimented and its efficacy proven in improving self-efficacy, mental health, and activities of daily living, and detailed results have been published. The LAY intervention is described according to the TIDieR checklist

Innovation and development after the earthquake in Emilia

The 2012 earthquake in Emilia-Romagna (Italy) has shaken up the collective understanding on the socioeconomic importance of a vast territory that generates almost 2% of Italian GDP. The area affected by the earthquake is characterized by the presence of important industrial and agricultural districts, and by good practices of local governance that are internationally renowned. Private and public buildings, factories, offices and retail shops, historical and cultural heritage sites have been severely damaged. Not only, but it set in motion transformations in the socio-economic system that might have unexpected consequences and that undermine the quick recovery of the local system: different agents, at different levels, taking individual and collective decisions, generate a cascade of changes that interact with its evolution path. Indeed, earthquakes pose challenges, but provide unprecedented opportunities: strategic decisions by economic and political agents, newly available financial resources, coordination or lack of coordination among main stakeholders, and so on. The following paper provides an overview of the first results of Energie Sisma Emilia research project: it aims at collecting and disseminating relevant knowledge and evidence in order to design policies. In particular, it identifies the agents propelling innovation processes, and analyses their strategies in ever-changing environment. The paper starts with a socio-economic analysis of the area struck by the earthquake, followed by the results of three of the focus groups conducted. Eventually, it illustrates a specific innovation: the introduction and implementation of the digital infrastructure “Mude”

Effetti economici e sociali del sisma sugli allevamenti dell’Alto Maceratese

Il contributo analizza la filiera zootecnica dell’Alto Maceratese danneggiata dal sisma del 2016. Rispetto ad alcune difficolta storiche (marginalità territoriale e spopolamento), questa analisi si è concentrata sugli effetti del sisma. In collaborazione con i veterinari della Regione Marche, è stato somministrato ad un campione di 55 famiglie di allevatori un questionario semi-strutturato al fine di osservare le caratteristiche socio-economiche degli allevamenti (prevalentemente a media quota e dediti nella metà dei casi unicamente all’attività zootecnica) e i danni prodotti dal sisma (ad abitazioni, patrimonio zootecnico e strutture agricole). L’ultima sezione del questionario raccoglie alcune proposte di miglioramento nella fase di ricostruzione. Proprio la rigenerazione dei territori colpiti, infatti, sembra porre ambizione sfide che riguardano il ripristino delle attività lavorative e il ritorno della popolazione sul territorio

Cross-cultural adaptation and validation of the “spinal cord injury-falls concern scale” in the Italian population

Study design: Psychometrics study. Objective: The objective of this study was to develop an Italian version of the Spinal Cord Injury-Falls Concern Scale (SCI-FCS) and examine its reliability and validity. Setting: Multicenter study in spinal units in Northern and Southern Italy. The scale also was administered to non-hospitalized outpatient clinic patients. Methods: The original scale was translated from English to Italian using the “Translation and Cultural Adaptation of Patient-Reported Outcomes Measures” guidelines. The reliability and validity of the culturally adapted scale were assessed following the “Consensus-Based Standards for the Selection of Health Status Measurement Instruments” checklist. The SCI-FCS-I internal consistency, inter-rater, and intra-rater reliability were examined using Cronbach’s alpha coefficient and the intraclass correlation coefficient, respectively. Concurrent validity was evaluated using Pearson’s correlation coefficient with the Italian version of the short form of the Wheelchair Use Confidence Scale for Manual Wheelchair Users (WheelCon-M-I-short form). Results: The Italian version of the SCI-FCS-I was administered to 124 participants from 1 June to 30 September 2017. The mean ± SD of the SCI-FCS-I score was 16.73 ± 5.88. All SCI-FCS items were either identical or similar in meaning to the original version’s items. Cronbach’s α was 0.827 (p < 0.01), the inter-rater reliability was 0.972 (p < 0.01), and the intra-rater reliability was 0.973 (p < 0.01). Pearson’s correlation coefficient of the SCI-FCS-I scores with the WheelCon-M-I-short form was 0.56 (p < 0.01). Conclusions: The SCI-FCS-I was found to be reliable and a valid outcome measure for assessing manual wheelchair concerns about falling in the Italian population