The findings suggest that unfavourable psychosocial work environment predicts ITL in Chinese nurses. Improvements in the psychosocial work environment may be helpful in retention of the nursing workforce.
We address the problem of finding a "best" deterministic query answer to a query over a probabilistic database. For this purpose, we propose the notion of a consensus world (or a consensus answer) which is a deterministic world (answer) that minimizes the expected distance to the possible worlds (answers). This problem can be seen as a generalization of the well-studied inconsistent information aggregation problems (e.g. rank aggregation) to probabilistic databases. We consider this problem for various types of queries including SPJ queries, Top-k queries, group-by aggregate queries, and clustering. For different distance metrics, we obtain polynomial time optimal or approximation algorithms for computing the consensus answers (or prove NP-hardness). Most of our results are for a general probabilistic database model, called and/xor tree model, which significantly generalizes previous probabilistic database models like x-tuples and block-independent disjoint models, and is of independent interest.
Cystic fibrosis (CF) is a life‐limiting disease caused by defective or deficient cystic fibrosis transmembrane conductance regulator (CFTR) activity. The recent US Food and Drug Administration (FDA) approval of lumacaftor combined with ivacaftor (Orkambi) targets patients with the F508del‐CFTR. The question remains: Is this breakthrough combination therapy the “magic‐bullet” cure for the vast majority of patients with CF? This review covers the contemporary clinical and scientific knowledge‐base for lumacaftor/ivacaftor and highlights the emerging issues from recent conflicting literature reports.
Probabilistic database systems have successfully established themselves as a tool for managing uncertain data. However, much of the research in this area has focused on efficient query evaluation and has largely ignored two key issues that commonly arise in uncertain data management: First, how to provide explanations for query results, e.g., "Why is this tuple in my result?" or "Why does this output tuple have such high probability?". Second, the problem of determining the sensitive input tuples for the given query, e.g., users are interested to know the input tuples that can substantially alter the output, when their probabilities are modified (since they may be unsure about the input probability values). Existing systems provide the lineage/provenance of each of the output tuples in addition to the output probabilities, which is a boolean formula indicating the dependence of the output tuple on the input tuples. However, lineage does not immediately provide a quantitative relationship and it is not informative when we have multiple output tuples. In this paper, we propose a unified framework that can handle both the issues mentioned above to facilitate robust query processing. We formally define the notions of influence and explanations and provide algorithms to determine the top-influential set of variables and the top-set of explanations for a variety of queries, including conjunctive queries, probabilistic threshold queries, top-k queries and aggregation queries. Further, our framework naturally enables highly efficient incremental evaluation when input probabilities are modified (e.g., if uncertainty is resolved). Our preliminary experimental results demonstrate the benefits of our framework for performing robust query processing over probabilistic databases.
Metacognition, self-efficacy, and motivation are important components of interaction in self-regulated learning (SRL). However, the psychological mechanism underlying the association among them in mathematical learning remained ambiguous. The present study investigated whether the relationship between metacognitive knowledge (MK) and mathematics performance can be mediated by self-efficacy and motivation. The sample comprised 569 students (245 male, Mage = 16.39, SD = 0.63) of Grade 10 in China. The MK in mathematics questionnaire, the self-efficacy questionnaire, the academic motivation scale, Raven advanced progressive matrix, and mathematics tests were used for data collection. Our results suggested that the mathematics performance could be predicted by MK, self-efficacy and intrinsic motivation. Moreover, the association between MK and mathematics performance was mediated by self-efficacy and intrinsic motivation, as revealed by a multiple mediation analysis. Additionally, there were sex differences in MK, self-efficacy and intrinsic motivation. The findings highlight the psychological mechanism in the mathematics of Chinese students and will help teachers to improve students’ mathematical learning in SRL framework more effectively. Implications for education and further studies are discussed.
Objectives: Work stress is an emergent risk in occupational health in China, and its measurement is still a critical issue. The aim of this study was to examine the reliability and validity of a short version of the effortreward imbalance (ERI) questionnaire in a sample of Chinese workers. Methods: A community-based survey was conducted in 1,916 subjects aged 30−65 years with paid employment (971 men and 945 women). Results: Acceptable internal consistencies of the three scales, effort, reward and overcommitment, were obtained. Confirmatory factor analysis showed a good model fit of the data with the theoretical structure (goodness-of-fit index=0.95). Evidence of criterion validity was demonstrated, as all three scales were independently associated with elevated odds ratios of both poor physical and mental health. Conclusions: Based on the findings of our study, this short version of the ERI questionnaire is considered to be a reliable and valid tool for measuring psychosocial work environment in Chinese working populations. (J Occup Health 2012; 54: 427-433)
Self-determination theory (SDT) has contributed greatly to our understanding of human motivation. Based on SDT, the Academic Motivation Scale (AMS) was developed to assess students’ motivation to learn. AMS has been successfully applied to the educational context in Western cultures. However, no psychometrically validated version is available in China. The present study aimed to revise and validate AMS in China. The AMS was administered to 882 traditional high school students and 419 vocational high students. A retest was administered to 67 traditional high school students 2 months later. Confirmatory factor analysis (CFA) demonstrated that the seven-factor model fitted the data well in both samples. Further analysis revealed that each subscale showed satisfactory Cronbach’s alpha and test–retest reliability. The AMS also showed significant correlations with criteria such as basic psychological needs, school satisfaction, perceived autonomy in the classroom, and other motivational counterparts, demonstrating good criterion-related validity. Group comparison showed that traditional high school students were more intrinsically motivated, less extrinsically motivated, and less amotivated than vocational high school students, providing support for its discriminant validity. In conclusion, the Chinese version of AMS was psychometrically sound and could be applied in China.
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