Academic achievement following childhood brain disease: implications for the concept of learning disabilities.

The present study examined three hypotheses regarding the consequences of early brain damage for academic achievement: First, early brain insults will have a negative impact on achievement, even in children with normal intelligence. Second, underachievement in these children will be at least partially independent of IQ (i.e., not fully accounted for by a lowering of IQ within the average range). Third, normally intelligent children with histories of brain insult will also manifest selective cognitive dysfunctions. To test these hypotheses, we compared two groups of children who had recovered from Haemophilus influenzae type b meningitis. The "complicated" group consisted of children who, as a consequence of having sustained neurologic complications during their illness, were more likely to have had brain insults. Children in the "uncomplicated" group did not have complications with their illness and were regarded as having escaped significant central nervous system (CNS) pathology. Only children with normal hearing and a prorated Full Scale IQ of at least 80 (WISC-R) were considered. Group differences on the Wide Range Achievement Test-Revised were consistent with the first two hypotheses. Although the two groups had similar Verbal IQs, the complicated group also had a lower mean Performance IQ and performed less well on perceptually demanding neuropsychological tasks. Findings suggest that learning disabilities may have selective, brain-related cognitive antecedents; but they challenge the practice of using IQ criteria for clinical diagnosis.     

Effects of Mass and Interpersonal Communication on Breast Cancer Screening: Advancing Agenda-Setting Theory in Health Contexts

Drawing on components of agenda-setting theory and the two-step flow of information from mass media to news audiences, this study examines the effects of mass and interpersonal communication on breast cancer screening practices among college- and middle-aged women (n = 284). We theorized that screening behaviors among younger women would be influenced more by interpersonal sources of information while screening among middle-aged women would be more influenced by exposure to mass-mediated information. Findings supported anticipated patterns, revealing important and varying roles for both mass and interpersonal communication in the health behaviors of women. Implications for health practitioners and campaign planners, as well as recommendations for future research, are discussed.     

Another Look at What Moves Public Opinion: Media Agenda Setting and Polls in the 2000 U.S. Election

This study examined two levels of media agenda effects on aggregatepublic opinion from different news sources. The effects wereinvestigated immediately and cumulatively. Content analysisdata from the 2000 U.S. presidential election coverage by fournational news organizations were related to the Gallup pre-electionpoll standings of each candidate. Regression analyses foundthat both the salience of a candidate and the salience of theattributes of a candidate cumulatively, but not immediately,influenced his standing in the polls. An analysis of news sourcessupported the finding that the two levels of agenda-settingeffects seemed mostly cumulative rather than immediate. Newsfrom different sources, however, tended to have effects of differentdegrees and sometimes different directions on candidate pollstandings. Cumulative effects of candidate salience on aggregateopinion change were found for non-partisan and neutral newssources—reporters, poll reporting and public documents—whereasthe effects of candidate attribute salience mostly came frompartisan sources—the candidate himself and members ofthe competing political party. Possible political implicationsof these findings are discussed.     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.