Training at the postgraduate level for medical librarians: a review.

Postgraduate education for medical librarians is approachable from several perspectives, including internships, certificate programs, and continuing education programs. The diverse population of medical library personnel calls for a varied yet coordinated system of postgraduate education involving the Medical Library Association, regional medical libraries, library schools, and the National Library of Medical, in addition to active participation by all librarians in the health sciences field. Basic philosophies for each of the major types of programs are discussed and recommendations for future training of health sciences librarians are provided.     

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.     

Construction of query concepts based on feature clustering of documents

In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on TREC retrieval.     

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.     

Books are silent ambassadors: The Noma award for the translation of Japanese literature

Summary Now more than ever good translations are proving to be vitally important to fulfilling Shoichi Noma’s mission of advancing good relationships between cultures. With the emergence of dozens of world-class Japanese writers, the translators of Japanese literature have acquired a new significance and a new importance. No longer are they required to simply handle the language like an exotic and precious commodity but they must prepare a writer’s work to compete with literature from around the globe and let others understand the issues at their very heart of the Japanese people. Indeed, the selection committee’s choice of Murakami’s Wind-Up Bird Chronicle is an apt example of how literature at its very best can span cultural divides to promote mutual understanding of very complicated and important world issues. Written for Japanese to help them understand the devastation of war, it is a rare and timely book that confronts Japan’s experience head on. Ironically, Noma’s idea that books are silent ambassadors, an idea born from agony and defeat, so many years later is helping the world come together towards a unified vision of peace and understanding. “Literature has been described as a means of exchange between the hearts of people. Literary translation, then, is the building of bridges so that all peoples of the world can interact in this way.” Ye Weiqu, Chinese Institute of Social Studies.     

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.