eGovernment and organizational changes: towards anextended governance model

Over the last decade the diffusion of Information Technologies has represented one of the main drivers of government reform. The adoption process of such technologies has posed significant challenges for public organizations. The aim of this paper is thus to look into the process of organizational change that public agencies have undergone, in order to single out its most salient characteristics, such as understanding changes in the adoption of technologies, in organizational choices, in skill needs and in customer-public administrations relationship. On the one hand, organizations are gradually opening up their institutional boundaries in order to proactively answer to environmental changes. On the other hand, citizens play an increasing role in the context of e-Government, since their suggestions and contributions may considerably influence decisions taken by public administrations. Specifically, we attempt to answer this research question: What are e-Government organizational implications in the back office and in the interaction with citizens due to Information Technologies diffusion? Using data from a survey on 1,206 Italian public administrations, we show how organizational changes are emerging, based on the overcoming of traditional bureaucratic organizational forms. The implications of these findings are also discusse

Making Sense of Gov 2.0 Strategies: "No Citizens, No Party"

One of the main factors contributing to the limited impact of eParticipation projects is the presence of a high level of social complexity that has been identified by Macintosh as one of the five challenges in the implementation of eParticipation practices. How to make sense of social complexity is still an open issue as well as the way governments can take benefit from the wealth of information that is already available on their constituencies' collective behaviour. In this paper, we contend that the presence of a considerable variance in terms of political interests, educational level and technological skills makes it very difficult to design workable and effective systems to support participation. A modular strategy is then recommended requiring policy designers to make a step towards citizens rather than expecting the citizenry to move their content production activity onto the "official" spaces created for ad hoc participation

Evaluating Advanced Forms of Social Media Use in Government

Government agencies gradually start moving from simpler to more advanced forms of social media use, which are characterized by higher technological and political complexity. It is important to evaluate systematically these efforts based on sound theoretical foundations. In this direction this paper outlines and evaluates an advanced form of automated and centrally managed combined use of multiple social media by government agencies for promoting participative public policy making. For this purpose an evaluation framework has been developed, which includes both technological and political evaluation, and focuses on the fundamental complexities and challenges of these advanced forms of social media exploitation. It has been used for the evaluation of a pilot application of this approach for conducting a consultation campaign concerning the large scale application of a telemedicine program in Piedmont, Italy, revealing its important potential and strengths, and at the same time some notable problems and weaknesses as well

Hydroxychloroquine and chloroquine retinal safety concerns during COVID-19 outbreak

Purpose: The current coronavirus disease 2019 (COVID-19) has been declared by the World Health Organization a global pandemic. Chloroquine (CQ) and hydroxychloroquine (HCQ) have been largely adopted in the clinical setting for the management of SARS-CoV-2 infection; however, their known retinal toxicity has raised some safety concerns, especially considering the higher-dosage employed for COVID-19 patients as compared with their suggested posology for their usual indications, including systemic lupus erythematosus and other rheumatic diseases. In this review, we will discuss the optimal dosages recommended for COVID-19 patients when treated with HCQ and CQ. Methods: A comprehensive literature search was performed in PubMed, Cochrane library, Embase and Scopus, by using the following search terms: "chloroquine retinal toxicity" and "hydroxychloroquine retinal toxicity" alone or in combination with "coronavirus", "COVID-19", " SARS-CoV-2 infection " from inception to August 2020. Results: Although there is still no consistent evidence about HCQ/CQ retinal toxicity in patients with COVID-19, these possible drug-related retinal adverse events may represent a major safety concern. For this reason, appropriate screening strategies, including telemedicine, should be developed in the near future. Conclusion: A possible future clinical perspective for patients with COVID-19 treated with HCQ/CQ could reside in the multidisciplinary collaboration between ophthalmologists monitoring the risk of HCQ/CQ-related retinal toxicity and those physicians treating COVID-19 infection

Knowledge Graph Embeddings with node2vec for Item Recommendation

In the past years, knowledge graphs have proven to be beneficial for recommender systems, efficiently addressing paramount issues such as new items and data sparsity. Graph embeddings algorithms have shown to be able to automatically learn high quality feature vectors from graph structures, enabling vector-based measures of node relatedness. In this paper, we show how node2vec can be used to generate item recommendations by learning knowledge graph embeddings. We apply node2vec on a knowledge graph built from the MovieLens 1M dataset and DBpedia and use the node relatedness to generate item recommendations. The results show that node2vec consistently outperforms a set of collaborative filtering baselines on an array of relevant metric

An empirical comparison of knowledge graph embeddings for item recommendation

In the past years, knowledge graphs have proven to be beneficial for recommender systems, efficiently addressing paramount issues such as new items and data sparsity. At the same time, several works have recently tackled the problem of knowledge graph completion through machine learning algorithms able to learn knowledge graph embeddings. In this paper, we show that the item recommendation problem can be seen as a specific case of knowledge graph completion problem, where the “feedback” property, which connects users to items that they like, has to be predicted. We empirically compare a set of state-of-the-art knowledge graph embeddings algorithms on the task of item recommendation on the Movielens 1M dataset. The results show that knowledge graph embeddings models outperform traditional collaborative filtering baselines and that TransH obtains the best performance