BACKGROUND AND OBJECTIVES: No practical method for identifying patients with low heath literacy exists. We sought to develop screening questions for identifying patients with inadequate or marginal health literacy. METHODS: Patients (n=332) at a VA preoperative clinic completed in-person interviews that included 16 health literacy screening questions on a 5-point Likert scale, followed by a validated health literacy measure, the Short Test of Functional Health Literacy in Adults (STOHFLA). Based on the STOFHLA, patients were classified as having either inadequate, marginal, or adequate health literacy. Each of the 16 screening questions was evaluated and compared to two comparison standards: (1) inadequate health literacy and (2) inadequate or marginal health literacy on the STOHFLA. RESULTS: Fifteen participants (4.5%) had inadequate health literacy and 25 (7.5%) had marginal health literacy on the STOHFLA. Three of the screening questions, "How often do you have someone help you read hospital materials?" "How confident are you filling out medical forms by yourself?" and "How often do you have problems learning about your medical condition because of difficulty understanding written information?" were effective in detecting inadequate health literacy (area under the receiver operating characteristic curve of 0.87, 0.80, and 0.76, respectively). These questions were weaker for identifying patients with marginal health literacy. CONCLUSIONS: Three questions were each effective screening tests for inadequate health literacy in this population.
BACKGROUND: Supporting 21st century health care and the practice of evidence-based medicine (EBM) requires ubiquitous access to clinical information and to knowledge-based resources to answer clinical questions. Many questions go unanswered, however, due to lack of skills in formulating questions, crafting effective search strategies, and accessing databases to identify best levels of evidence. METHODS: This randomized trial was designed as a pilot study to measure the relevancy of search results using three different interfaces for the PubMed search system. Two of the search interfaces utilized a specific framework called PICO, which was designed to focus clinical questions and to prompt for publication type or type of question asked. The third interface was the standard PubMed interface readily available on the Web. Study subjects were recruited from interns and residents on an inpatient general medicine rotation at an academic medical center in the US. Thirty-one subjects were randomized to one of the three interfaces, given 3 clinical questions, and asked to search PubMed for a set of relevant articles that would provide an answer for each question. The success of the search results was determined by a precision score, which compared the number of relevant or gold standard articles retrieved in a result set to the total number of articles retrieved in that set. RESULTS: Participants using the PICO templates (Protocol A or Protocol B) had higher precision scores for each question than the participants who used Protocol C, the standard PubMed Web interface. (Question 1: A = 35%, B = 28%, C = 20%; Question 2: A = 5%, B = 6%, C = 4%; Question 3: A = 1%, B = 0%, C = 0%) 95% confidence intervals were calculated for the precision for each question using a lower boundary of zero. However, the 95% confidence limits were overlapping, suggesting no statistical difference between the groups. CONCLUSION: Due to the small number of searches for each arm, this pilot study could not demonstrate a statistically significant difference between the search protocols. However there was a trend towards higher precision that needs to be investigated in a larger study to determine if PICO can improve the relevancy of search results.
Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQUADRUN, a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQUADRUN, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQUADRUN is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD achieves only 66% F1 on SQUADRUN. We release SQUADRUN to the community as the successor to SQuAD.
We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com
OBJECTIVE: To evaluate the 3 alcohol consumption questions from the Alcohol Use Disorders Identification Test (AUDIT-C) as a brief screening test for heavy drinking and/or active alcohol abuse or dependence. METHODS: Patients from 3 Veterans Affairs general medical clinics were mailed questionnaires. A random, weighted sample of Health History Questionnaire respondents, who had 5 or more drinks over the past year, were eligible for telephone interviews (N = 447). Heavy drinkers were oversampled 2:1. Patients were excluded if they could not be contacted by telephone, were too ill for interviews, or were female (n = 54). Areas under receiver operating characteristic curves (AUROCs) were used to compare mailed alcohol screening questionnaires (AUDIT-C and full AUDIT) with 3 comparison standards based on telephone interviews: (1) past year heavy drinking (>14 drinks/week or > or =5 drinks/ occasion); (2) active alcohol abuse or dependence according to the Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition, criteria; and (3) either. RESULTS: Of 393 eligible patients, 243 (62%) completed AUDIT-C and interviews. For detecting heavy drinking, AUDIT-C had a higher AUROC than the full AUDIT (0.891 vs 0.881; P = .03). Although the full AUDIT performed better than AUDIT-C for detecting active alcohol abuse or dependence (0.811 vs 0.786; P<.001), the 2 questionnaires performed similarly for detecting heavy drinking and/or active abuse or dependence (0.880 vs 0.881). CONCLUSIONS: Three questions about alcohol consumption (AUDIT-C) appear to be a practical, valid primary care screening test for heavy drinking and/or active alcohol abuse or dependence.
We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.
This is a chapter that asks questions about where we are with politics now that actor network theory and its semiotic relatives have reshaped ontology. They have reshaped it by underlining that the reality we live with is one performed in a variety of practices. The radical consequence of this is that reality itself is multiple. An implication of this might be that there are options between the various versions of an object: which one to perform? But if this were the case then we would need to ask where such options might be situated and what was at stake when a decision between alternative performances was made. We would also need to ask to what extent are there options between different versions of reality if these are not exclusive, but, if they clash in some places, depend on each other elsewhere. The notion of choice also presupposes an actor who actively chooses, while potential actors may be inextricably linked up with how they are enacted. These various questions are not answered, but illustrated with the example of anaemia, a common deviance that comes in (at least) clinical, statistical and pathophysiological forms.
The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
R. M. Baron and D. A. Kenny (1986; see record 1987-13085-001) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data. Graduating from cross-sectional to longitudinal designs enables researchers to make more rigorous inferences about the causal relations implied by such models. In this transition, misconceptions and erroneous assumptions are the norm. First, we describe some of the questions that arise (and misconceptions that sometimes emerge) in longitudinal tests of mediational models. We also provide a collection of tips for structural equation modeling (SEM) of mediational processes. Finally, we suggest a series of 5 steps when using SEM to test mediational processes in longitudinal designs: testing the measurement model, testing for added components, testing for omitted paths, testing the stationarity assumption, and estimating the mediational effects.
The literature on identification in organizations is surprisingly diverse and large. This article reviews the literature in terms of four fundamental questions. First, under “What is identification?,” it outlines a continuum from narrow to broad formulations and differentiates situated identification from deep identification and organizational identification from organizational commitment. Second, in answer to “Why does identification matter?,” it discusses individual and organizational outcomes as well as several links to mainstream organizational behavior topics. Third, regarding “How does identification occur?,” it describes a process model that involves cycles of sensebreaking and sensegiving, enacting identity and sensemaking, and constructing identity narratives. Finally, under “One or many?,” it discusses team, workgroup, and subunit; relational; occupational and career identifications; and how multiple identifications may conflict, converge, and combine.
This paper will discuss eight frequently asked questions about public corruption: (1) What is corruption? (2) Which countries are the most corrupt? (3) What are the common characteristics of countries with high corruption? (4) What is the magnitude of corruption? (5) Do higher wages for bureaucrats reduce corruption? (6) Can competition reduce corruption?( 7) Why have there been so few (recent) successful attempts to fight corruption? (8) Does corruption adversely affect growth?
This study compared three methods of collecting survey data about sexual behaviors and other sensitive topics: computer-assisted personal interviewing (CAPI), computer-assisted self-administered interviewing (CASI), and audio computer-assisted self-administered interviewing (ACASI). Interviews were conducted with an area probability sample of more than 300 adults in Cook County, Illinois. The experiment also compared open and closed questions about the number of sex partners and varied the context in which the sex partner items were embedded. The three mode groups did not differ in response rates, but the mode of data collection did affect the level of reporting of sensitive behaviors: both forms of self-administration tended to reduce the disparity between men and women in the number of sex partners reported. Self-admimstration, especially via ACASI, also increased the proportion of respondents admitting that they had used illicit drugs. In addition, when the closed answer options emphasized the low end of the distribution, fewer sex partners were reported than when the options emphasized the high end of the distribution; responses to the open-ended versions of the sex partner items generally fell between responses to the two closed versions.
Humans continue to transform the global nitrogen cycle at a record pace, reflecting an increased combustion of fossil fuels, growing demand for nitrogen in agriculture and industry, and pervasive inefficiencies in its use. Much anthropogenic nitrogen is lost to air, water, and land to cause a cascade of environmental and human health problems. Simultaneously, food production in some parts of the world is nitrogen-deficient, highlighting inequities in the distribution of nitrogen-containing fertilizers. Optimizing the need for a key human resource while minimizing its negative consequences requires an integrated interdisciplinary approach and the development of strategies to decrease nitrogen-containing waste.
This review summarizes recent developments in the theory of spin glasses, as well as pertinent experimental data. The most characteristic properties of spin glass systems are described, and related phenomena in other glassy systems (dielectric and orientational glasses) are mentioned. The Edwards-Anderson model of spin glasses and its treatment within the replica method and mean-field theory are outlined, and concepts such as "frustration," "broken replica symmetry," "broken ergodicity," etc., are discussed. The dynamic approach to describing the spin glass transition is emphasized. Monte Carlo simulations of spin glasses and the insight gained by them are described. Other topics discussed include site-disorder models, phenomenological theories for the frozen phase and its excitations, phase diagrams in which spin glass order and ferromagnetism or antiferromagnetism compete, the Ne\'el model of superparamagnetism and related approaches, and possible connections between spin glasses and other topics in the theory of disordered condensed-matter systems.
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The use of interrater reliability (IRR) and interrater agreement (IRA) indices has increased dramatically during the past 20 years. This popularity is, at least in part, because of the increased role of multilevel modeling techniques (e.g., hierarchical linear modeling and multilevel structural equation modeling) in organizational research. IRR and IRA indices are often used to justify aggregating lower-level data used in composition models. The purpose of the current article is to expose researchers to the various issues surrounding the use of IRR and IRA indices often used in conjunction with multilevel models. To achieve this goal, the authors adopt a question-and-answer format and provide a tutorial in the appendices illustrating how these indices may be computed using the SPSS software.
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Scholarly interest in the study of trust and distrust in organizations has grown dramatically over the past five years. This interest has been fueled, at least in part, by accumulating evidence that trust has a number of important benefits for organizations and their members. A primary aim of this review is to assess the state of this rapidly growing literature. The review examines recent progress in conceptualizing trust and distrust in organizational theory, and also summarizes evidence regarding the myriad benefits of trust within organizational systems. The review also describes different forms of trust found in organizations, and the antecedent conditions that produce them. Although the benefits of trust are well-documented, creating and sustaining trust is often difficult. Accordingly, the chapter concludes by examining some of the psychological, social, and institutional barriers to the production of trust.
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A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN). Our model improves the state-of-the-art on the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA dataset. By using ResNet, the performance is further improved to 62.1% for VQA and 65.4% for COCO-QA.