Declaration of interest:Role of the funding source: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Author contributions: YHL designed the study. YHL and CHL conducted the study and analyzed the data. YHL and YCC drafted the manuscript. All authors contributed to data analysis, drafting or revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work. Highlights The increased Google searches for "wash hands", rather than "face mask", reducing the speed of COVID-19 outbreak during the following three weeks among 21 countries. Google search for "wash hands" indicated not only the promotion of hand hygiene awareness but might also reflect the extent that people proactively engaged in hand washing. Google searches for "wash hands" provide real-time indicators for transmissionreduction policies and population health literacy in the early stage of the COVID-19 outbreak.3 AbstractThis study hypothesized that national population health literacy might reflect on their keywords searching. We applied Google searches for "wash hands" and "face mask"

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Accurate estimation of licensed channel Primary User's (PU) temporal statistics is important for Dynamic Spectrum Access (DSA) systems. With accurate estimation of the mean duty cycle, u, and the mean off-and on-times of PUs, DSA systems can more efficiently assign PU resources to its subscribers, thus, increasing channel utilization. This paper presents a mathematical analysis of the accuracy of estimating u, as well as the PU mean off-and on-times, where the estimation accuracy is expressed in terms of the Cramér-Rao bound on the mean squared estimation error. The analysis applies for the traffic model assuming exponentially distributed PU off-and on-times, which is a common model in traffic literature. The estimation accuracy is quantified as a function of the number of samples and observation window length, hence, this work provides guidelines on traffic parameters estimation for both energy-constrained and delay-constrained applications. For estimating u, we derive the mean squared estimation error for uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood (ML) estimation. The estimation accuracy of the mean PU off-and on-times is studied when ML estimation is employed. Besides, the impact of spectrum sensing errors on the estimation accuracy is studied analytically for the averaging estimators, while simulation results are used for the ML estimators. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions. We show that the estimation error for all traffic parameters is lower bounded for a fixed observation window length due to the correlation between the traffic samples.On the other hand, the impact of spectrum sensing errors on the estimation error of u can be eliminated by increasing the number of traffic samples for a fixed observation window length. Finally, we prove that for estimating u under perfect knowledge of either the mean PU off-or on-time, ML estimation can yield the same estimation error as weighted sample averaging using only half the observation window length. I. INTRODUCTIONSpectrum sensing is the cornerstone of Dynamic Spectrum Access (DSA) [1] where Secondary Users (SUs) search for, and operate on, licensed spectrum that is temporarily vacant. The SUs have to sense for the presence of Primary (licensed) Users (PUs) on the targeted spectral bands before utilizing these radio resources. The PU channel utilization patterns are stochastic in nature [2]. Consequently, acquiring knowledge about the PU traffic statistics can improve the performance of SU channel selection algorithms, for example [3], and help in achieving more efficient resource allocation, for example [4], in DSA systems. A. The Need for Accurate PU Traffic Estimation: an ExampleThe multi-channel Medium Access Control (MAC) protocol proposed in [5] is a good example for showing the importance of PU traffic parameters estimation. In the proposed MAC protocol, the SUs access PU channels opportunistica...

BackgroundInternship, the transition period from medical student to junior doctor, is highly stressful for interns in the West; however, little is known about the experience of interns in coping with stress in Taiwan. This study aimed to develop a model for coping with stress among Taiwanese interns and to examine the relationship between stress and learning outcomes.MethodsFor this qualitative study, we used grounded theory methodology with theoretical sampling. We collected data through in-depth interviews and participant observations. We employed the constant comparative method to analyse the data until data saturation was achieved.ResultsThe study population was 124 medical interns in a teaching hospital in northern Taiwan; 21 interns (12 males) participated. Data analysis revealed that the interns encountered stressors (such as sense of responsibility, coping with uncertainty, and interpersonal relationships) resulting from their role transition from observer to practitioner. The participants used self-directed learning and avoidance as strategies to deal with their stress.ConclusionsA self-directed learning strategy can be beneficial for an intern’s motivation to learn as well as for patient welfare. However, avoiding stressors can result in less motivation to learn and hinder the quality of care. Understanding how interns experience and cope with stress and its related outcomes can help medical educators and policy makers improve the quality of medical education by encouraging interns’ self-directed learning strategy and discouraging the avoidance of stressors.