Anxiety, concerns and COVID-19: Cross-country perspectives from families and individuals with neurodevelopmental conditions

BACKGROUND: The COVID-19 pandemic had a major impact on the mental health and well-being of children with neurodevelopmental conditions (NDCs) and of their families worldwide. However, there is insufficient evidence to understand how different factors (e.g., individual, family, country, children) have impacted on anxiety levels of families and their children with NDCs developed over time. METHODS: We used data from a global survey assessing the experience of 8043 families and their children with NDCs (mean of age (m) = 13.18 years, 37% female) and their typically developing siblings (m = 12.9 years, 45% female) in combination with data from the European Centre for Disease Prevention and Control, the University of Oxford, and the Central Intelligence Agency (CIA) World Factbook, to create a multilevel data set. Using stepwise multilevel modelling, we generated child-, family- and country-related factors that may have contributed to the anxiety levels of children with NDCs, their siblings if they had any, and their parents. All data were reported by parents. RESULTS: Our results suggest that parental anxiety was best explained by family-related factors such as concerns about COVID-19 and illness. Children’s anxiety was best explained by child-related factors such as children’s concerns about loss of routine, family conflict, and safety in general, as well as concerns about COVID-19. In addition, anxiety levels were linked to the presence of pre-existing anxiety conditions for both children with NDCs and their parents. CONCLUSIONS: The present study shows that across the globe there was a raise in anxiety levels for both parents and their children with NDCs because of COVID-19 and that country-level factors had little or no impact on explaining differences in this increase, once family and child factors were considered. Our findings also highlight that certain groups of children with NDCs were at higher risk for anxiety than others and had specific concerns. Together, these results show that anxiety of families and their children with NDCs during the COVID-19 pandemic were predicted by very specific concerns and worries which inform the development of future toolkits and policy. Future studies should investigate how country factors can play a protective role during future crises

Finding Related Pages Using the Link Structure of the WWW

Most of the current algorithms for finding related pages are exclusively based on text corpora of the WWW or incorporate only authority or hub values of pages. In this paper, we present HubFinder, a new fast algorithm for finding related pages exploring the link structure of the Web graph. Its criterion for filtering output pages is \u94pluggable\u94, depending on the user\u92s interests, and may vary from global page ranks to text content, etc. We also introduce HubRank, a new ranking algorithm which gives a more complete view of page \u94importance\u94 by biasing the authority measure of PageRank towards hub values of pages. Finally, we present an evaluation of these algorithms in order to prove their qualities experimentally

Knowing Where to Search: Personalized Search Strategies for Peers In P2P Networks

Optimizing and focusing search and results ranking in P2P networks becomes more and more important with the increasing size of these networks. Even though a few approaches have already started to investigate the computation of PageRank-like values in P2P environments, none so far has investigated how personalization could be added to it. This paper tackles the problem of distributedly computing Personalized PageRank values in such a distributed environment and presents an algorithm which uses them to optimize and focus search in the P2P network. The paper also discusses how these algorithms improve current distributed search in power law networks and gives some simulation results

PROS: A Personalized Ranking Platform for Web Search

Current search engines rely on centralized page ranking algorithms which compute page rank values as single (global) values for each Web page. Recent work on topic-sensitive PageRank [6] and personalized PageRank [8] has explored how to extend PageRank values with personalization aspects. To achieve personalization, these algorithms need specific input: [8] for example needs a set of personalized hub pages with high PageRank to drive the computation. In this paper we show how to automate this hub selection process and build upon the latter algorithm to implement a platform for personalized ranking.We start from the set of bookmarks collected by a user and extend it to contain a set of hubs with high PageRank related to them. To get additional input about the user, we implemented a proxy server which tracks and analyzes user\u92s surfing behavior and outputs a set of pages preferred by the user. This set is then enrichened using our HubFinder algorithm, which finds related pages, and used as extended input for the [8] algorithm. All algorithms are integrated into a prototype of a personalized Web search system, for which we present a first evaluation. 1 Introduction Using the link structure of the World Wide Web to rank pages in search engines has been investigated heavily in recent years. The success of the Google Search Engine [5, 3] has inspired much of this work, but has lead also to the realization that further improvements are needed. The amount and diversity of Web pages (Google now indicates about 4.3 billion pages) lead researchers to explore faster and more personalized page ranking algorithms, in order to address the fact that some topics are covered by only a few thousand pages and some are covered by millions. For many topics, the existing PageRank algorithm is not sufficient to filter the results of a search engine query. Take for example the well-known query with the word \u93Java\u94 which should return top results for either the programming language or the island in the Pacific: Google definitively prefers the programming language because there are many more important pages on it than on the island. Moreover, most of the existing search engines focus only on answering user queries, although personalization will be more and more important as the amount of information available in the Web increases. Recently, several approaches to solve such problems have been investigated, building upon content analysis or on algorithms which build page ranks personalized for users or classes of users. The mos

Search Strategies for Scientific Collaboration Networks

Can we improve P2P search by looking into our social network? In this paper, we argue that P2P networks built upon specific communities (e.g., scientific social networks) could achieve such a goal, by providing an implicit personalization to the output results set. Existing work in social networks investigating co-authorship relations has shown that scientific collaboration networks are scale-free. At the same time, P2P systems based on synthesized small-world networks have emerged, with a positive impact on search efficiency. We propose to use existing social collaboration graphs as foundation for the P2P topology instead of creating purely technological topologies. To get an insight into the relationship between scientific collaboration and co-authorship, we compared both for an existing collaboration network. Based on this analysis, we then generated a large P2P collaboration network derived from co-authorship data collections as basis for our experiments. The most prevalent search type in the scientific context is keyword search for relevant publications. We investigate different search strategies suitable in that context and show our initial experimental results

Publish/Subscribe for RDF-based P2P Networks

Publish/subscribe systems are an alternative to query based systems in cases where the same information is asked for over and over, and where clients want to get updated answers for the same query over a period of time. Recent publish/subscribe systems such as P2P-DIET have introduced this paradigm in the P2P context. In this paper we built on the experience gained with P2P-DIET and the Edutella P2P infrastructure and present the first implementation of a P2P publish/subscribe system supporting metadata and a query language based on RDF. We define formally the basic concepts of our system and present detailed protocols for its operation. Our work utilizes the latest ideas in query processing for RDF data, P2P indexing and routing research

Designing Semantic Publish/Subscribe Networks Using Super-Peers

this paper, a very important contribution of SIENA is the adoption of a P2P model of interaction among servers (super-peers in our terminology) and the exploitation of traditional network algorithms based on shortest paths and minimum-weight spanning trees for routing messages. SIENA servers additionally utilize partially ordered sets encoding subscription and advertisement subsumption to minimize network tra#c. The core ideas of SIENA have recently been used in the pub/sub systems DIAS [23] and P2P-DIET [2, 24, 21] but now the data models utilized were inspired from Information Retrieval. DIAS and P2PDIET have also emphasized the use of sophisticated subscription indexing at each server to facilitate e#cient forwarding of notifications [40]. In some sense, the approach of DIAS and P2P-DIET puts together the best ideas from the database and distributed systems tradition in a single unifying framework. Another important contribution of P2P-DIET is that it demonstrates how to 18 P.- A. Chirita, S. Idreos, M. Koubarakis, W. Nejd

Personalized Reputation Management in P2P Networks

P2P networks have become increasingly popular in the recent years