BACKGROUND: In the Netherlands, the scope of dental hygiene practice was expanded in 2006. The objective of this study was to explore reasons among dentists and dental hygienists for supporting or opposing an extended scope of practice and to find explanatory factors.METHODS: A questionnaire containing pre-defined reasons and an open-ended question was distributed among 1,674 randomly selected members of two Dutch professional associations (874 dentists, 800 dental hygienists). Data were analyzed with binary logistic regression with Bayesian information criterion (BIC) model selection.RESULTS: Response were obtained from 541 practitioners (32.3%): i.e., 233 dentists (43.1%) and 308 dental hygienists (56.9%). Non-response analysis revealed no differences, and representativeness analysis showed similarities between samples and target populations. Most often, dentists reported flexible collaboration (50.2%) and dental hygienists indicated task variation (71.1%) as supportive reasons. As opposing reasons, dentists generally reported quality of care (41.2%) and dental hygienists' self-competence (22.7%). Reasons were explained by profession, gender, and new-style practitioners.CONCLUSION: Dentists and dental hygienists conveyed different reasons for supporting or opposing an extended scope of dental hygiene practice. Outcomes can be categorized as reasons related to economic, professional status, quality, job satisfaction, and flexible collaboration and are not only explained by profession.
This study focuses on SME networks of design and high-tech companies in Southeast Netherland. By highlighting the personal networks of members across design and high-tech industries, the study attempts to identify the main brokers in this dynamic environment. In addition, we investigate whether specific characteristics are associated with these brokers. The main contribution of the paper lies in the fact that, in contrast to most other work, it is quantitative and that it focuses on brokers identified in an actual network (based on both suppliers and users of the knowledge infrastructure). Studying the phenomenon of brokerage provides us with clear insights into the concept of brokerage regarding SME networks in different fields. In particular we highlight how third parties contribute to the transfer and development of knowledge. Empirical results show, among others that the most influential brokers are found in the nonprofit and science sector and have a long track record in their branch.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
