It is possible to draw up profiles of the students most vulnerable to tobacco advertising, and to cluster them in two groups, the "vitalists" and the "credulous." The effect of cigarette ads is different between these groups. This study can help to orientate smoking prevention.

The increasing number of patients receiving home respiratory therapy (HRT) is imposing a major impact on routine clinical care and healthcare system sustainability. The current challenge is to continue to guarantee access to HRT while maintaining the quality of care. The patient experience is a cornerstone of high-quality healthcare and an emergent area of clinical research. This review approaches the assessment of the patient experience in the context of HRT while highlighting the European contribution to this body of knowledge. This review demonstrates that research in this area is still limited, with no example of a prescription model that incorporates the patient experience as an outcome and no specific patient-reported experience measures (PREMs) available. This work also shows that Europe is leading the research on HRT provision. The development of a specific PREM and the integration of PREMs into the assessment of prescription models should be clinical research priorities in the next several years.

Population attitudes towards changing certain dietary behaviours were associated with having the greatest reliance on GPs.

Uncertainty propagation is an established approach to handle noisy and reverberant conditions in automatic speech recognition (ASR), but it has little been studied for speaker recognition so far. Yu et al. recently proposed to propagate uncertainty to the Baum-Welch (BW) statistics without changing the posterior probability of each mixture component. They obtained good results on a small dataset (YOHO) but little improvement on the NIST-SRE dataset, despite the use of oracle uncertainty estimates. In this paper, we propose to modify the computation of the posterior probability of each mixture component in order to obtain unbiased BW statistics. We show that our approach improves the accuracy of BW statistics on the Wall Street Journal (WSJ) corpus, but yields little or no improvement on NIST-SRE again. We provide a theoretical explanation for this that opens the way for more efficient exploitation of uncertainty on NIST-SRE and other large datasets in the future.

The growing demand for robust speech processing applications able to operate in adverse scenarios calls for new evaluation protocols and datasets beyond artificial laboratory conditions. The characteristics of real data for a given scenario are rarely discussed in the literature. As a result, methods are often tested based on the author expertise and not always in scenarios with actual practical value. This paper aims to open this discussion by identifying some of the main problems with data simulation or collection procedures used so far and summarizing the important characteristics of real scenarios to be taken into account, including the properties of reverberation, noise and Lombard effect. At last, we provide some preliminary guidelines towards designing experimental setup and speech recognition results for proposal validation.