BackgroundDespite a growing body of scientific literature exploring the nature of meditation there is limited information on the characteristics of individuals who use it. This is particularly true of comparative studies examining prevalence and predictors of use of various forms of meditation.MethodsA secondary analysis was conducted using data from the 2012 National Health Interview Survey (n = 34,525). Three popular forms of meditation were compared—mantra, mindfulness, and spiritual—to determine lifetime and 12-month use related to key sociodemographic, health behavior, health status, and healthcare access variables.ResultsThe 12-month prevalence for meditation practice was 3.1% for spiritual meditation, 1.9% for mindfulness meditation, and 1.6% for mantra meditation. This represents approximately 7.0, 4.3, and 3.6 million adults respectively. A comparison across the three meditation practices found many similarities in user characteristics, suggesting interest in meditation may be more related to the type of person meditating than to the type of practice selected. Across meditation styles use was more prevalent among respondents who were female, non-Hispanic White, college educated, physically active; who used other complementary health practices; and who reported depression. Higher utilization of conventional healthcare services was one of the strongest predictors of use of all three styles. In addition to similarities, important distinctions were observed. For example, spiritual meditation practice was more prevalent among former drinkers. This may reflect use of spiritual meditation practices in support of alcohol treatment and sobriety. Reasons for use of meditation were examined using the sample of respondents who practiced mindfulness meditation. Wellness and prevention (74%) was a more common reason than use to treat a specific health condition (30%). Common reasons for use included stress management (92%) and emotional well-being (91%), and to support other health behaviors. Meditation was viewed positively because it was self-care oriented (81%) and focused on the whole person (79%).ConclusionMeditation appears to provide an accessible, self-care resource that has potential value for mental health, behavioral self-regulation, and integrative medical care. Considering consumer preference for distinct types of meditation practices, understanding the underlying mechanisms, benefits, and applications of practice variations is important.
This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 De-identification Challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge.
This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clusters terms based on ontology metric, a score indicating semantic distance; and transforms the task into a multi-criteria optimization based on minimization of taxonomy structures and modeling of term abstractness. It combines the strengths of both lexico-syntactic patterns and clustering through incorporating heterogeneous features. The flexible design of the framework allows a further study on which features are the best for the task under various conditions. The experiments not only show that our system achieves higher F1-measure than other state-of-the-art systems, but also reveal the interaction between features and various types of relations, as well as the interaction between features and term abstractness.
Many requirements documents are written in natural language (NL). However, with the flexibility of NL comes the risk of introducing unwanted ambiguities in the requirements and misunderstandings between stakeholders. In this paper, we describe an automated approach to identify potentially nocuous ambiguity, which occurs when text is interpreted differently by different readers. We concentrate on anaphoric ambiguity, which occurs when readers may disagree on how pronouns should be interpreted.We describe a number of heuristics, each of which captures information that may lead a reader to favour a particular interpretation of the text. We use these heuristics to build a classifier, which in turn predicts the degree to which particular interpretations are preferred. We collected multiple human judgements on the interpretation of requirements exhibiting anaphoric ambiguity, and show how the distribution of these judgements can be used to assess whether a particular instance of ambiguity is nocuous. Given a requirements document written in natural language, our approach can identify sentences which contain anaphoric ambiguity, and use the classifier to alert the requirements writer of text that runs the risk of misinterpretation.We report on a series of experiments that we conducted to evaluate the performance of the automated system we developed to support our approach. The results show that the system achieves high recall with a consistent improvement on baseline precision subject to some ambiguity tolerance levels, allowing us to explore and highlight realistic and potentially problematic ambiguities in actual requirements documents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.