Tilburg University

About: Tilburg University is a education organization based out in Tilburg, Noord-Brabant, Netherlands. It is known for research contribution in the topics: Population & Context (language use). The organization has 5550 authors who have published 22330 publications receiving 791335 citations.

Predictors of treatment dropout in self-guided web-based interventions for depression: an 'individual patient data' meta-analysis

Abstract: Background It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions. Method A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined. Results Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94). Conclusions Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.

Popular support for institutionalised solidarity: a comparison between European welfare states

Abstract: In this article, we study the determinants of supportiveness for the welfare state as a system of institutionalised solidarity. We distinguish between two types of support; namely, 1) whether people hold the state responsible for achieving social-economic security and distributive justice, and 2) people's preference for the range of these goals that should be realised if the state is indeed held responsible. Using data from the Eurobarometer survey series, we investigate how, and to what extent, both kinds of support for the welfare state are related to position in the stratification structure, demographic characteristics, and social-political beliefs, as well as to features of European welfare state regimes. The results of a two-level hierarchical model suggest that moral commitment to the welfare state dominates at the individual level, whereas self-interest enters the picture mainly if a person is highly dependent on the provisions of the welfare state. Further, the findings give no support to the claim of a systematic variation between levels of popular support for the welfare state and its institutional set-up.

Productivity of older workers: perceptions of employers and employees.

Abstract: What determines the perceived productivity of the older worker and how does this perception compare to the perception of the productivity of the younger worker? In this study we present evidence based on data from Dutch employers and employees. Productivity perceptions are affected by one's age and one's position in the hierarchy. The young favor the young, the old favor the old, and employers value the productivity of workers less than employees do. However, there are also remarkable similarities across employers and employees. By distinguishing the various dimensions that underlie the productivity of younger and older workers, we tested whether soft qualities and abilities—e.g., reliability and commitment—are just as important as hard qualities—cognitive and physically based skills—in the eyes of both employers and employees. It appears that both employers and employees, young and old, view hard skills as far more important than soft skills.

Agent-oriented technology in support of e-business

Abstract: he rapid growth of the Internet, networking systems such as electronic data interchange systems, and the penetration of ISDNbased applications are stimulating an ever-increasing number of businesses to participate in e-commerce worldwide. For example, businesses use the Web to improve internal communication, help manage supply chains, conduct technical and market research, and locate potential partners. Moreover, innovative enterprises with good partner relationships are beginning to capitalize on the enormous potential of new global networking possibilities and are beginning to share sales data, customer buying patterns, and future plans with their suppliers and customers. One of the key characteristics of the e-business world is that companies will inevitably move more and more into a customer-centric paradigm in order to increase competitiveness. Customer behavior cannot be accurately predicted using traditional analytic methods like forecasting or budgeting. Instead, companies seeking a competitive edge will investigate other kinds of analytical methods based on, for example, heuristics and AI techniques. Intelligent business agents are the next higher level of abstraction in model-based solutions to business-to-business e-commerce applications. By building on the distributed object foundation, agent technology can help bridge the remaining gap between flexible design and usable applications. Agents support a natural merging of object orientation and knowledge-based technologies. They can facilitate the incorporation of reasoning capabilities within the business application logic (for example, encapsulation of business rules within agents or modeled organizations). They permit the inclusion of learning and selfimprovement capabilities at both infrastructure (adaptive routing) and application (adaptive user interfaces) levels. Unlike objects, business agents can participate in high-level (task-oriented) dialogues through the use of interaction protocols in conjunction with built-in organizational knowledge. In many cases, the need for communication is greatly reduced, as within these high-level dialogues, complex packets of procedural and declarative knowledge as well as state information may be exchanged in the form of mobile objects. In addition, agent technology can help address serious technological challenges such as concerns about effective searching, security and privacy, and effective use of interoperability between diverse business processes and diverse information required to achieve tele-cooperation and global e-commerce. The opportunities for using intelligent agents in an e-business application are enormous. For example, they can be used for real-time pricing and auctioning, involving different parties in a supply-chain network. Suppliers can present their products on the Web and

Forgetting Exceptions is Harmful in Language Learning

Abstract: We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging, prepositional-phrase attachment, and base noun phrase chunking. In a first series of experiments we combine memory-based learning with training set editing techniques, in which instances are edited based on their typicality and class prediction strength. Results show that editing exceptional instances (with low typicality or low class prediction strength) tends to harm generalization accuracy. In a second series of experiments we compare memory-based learning and decision-tree learning methods on the same selection of tasks, and find that decision-tree learning often performs worse than memory-based learning. Moreover, the decrease in performance can be linked to the degree of abstraction from exceptions (i.e., pruning or eagerness). We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.