A description of the catalog division project at the College of Physicians of Philadelphia Library.

This paper describes the procedures used at the Library of the College of Physicians of Philadelphia to divide its ninety-year-old dictionary card catalog. The division was necessitated by overcrowding, obsolete subject headings, and lack of a complete authority list which resulted in like materials being scattered throughout the catalog under several headings. Two catalogs were created: the historical-biographical catalog, representing all works published before 1950 and all works of historical or biographical nature; and the current catalog, containing all works published from 1950 on, excepting historical or biographical materials. The 1950- catalog was further divided into name and subject catalogs, and the subject section was revised according to MeSH. The project was completed in about two years. As a result, searching time has been much reduced, and the library is able to take advantage of the annual revisions of MeSH to update the subject catalog.     

介绍国外权威文献传递服务机构

目前网上文献传递服务在各大图书馆和情报机构都有一定的发展。本文主要从文献传递的特点、文献收藏、服务情况、服务周期、服务对象和服务费用等方面对国外几家较著名的网上文献情报机构进行介绍,以期对我们的文献传递服务工作提供帮助和参考。     

Communicating Prevention: The Effects of the keepin' it REAL Classroom Videotapes and Televised PSAs on Middle-School Students' Substance Use

This study sought to determine if exposure to two communication-oriented activities, videotapes and public service announcements, accounts for changes in substance use among adolescents participating in the Drug Resistance Strategies Project's keepin' it REAL adolescent substance use prevention curriculum. Middle-school students (4,734, 72% Latino) responded to questionnaires related to these analyses. An analysis of covariance (ANCOVA) model was fit separately to six substance use outcomes. The results suggested that intervention students who saw four or five videos engaged in less substance use in the past month than did students who saw fewer videos. Having seen the PSAs one or more times did not predict the reported change in substance use.     

The use of personal digital assistants in the health sciences: results of a survey.

OBJECTIVES: The purpose of this study is to determine how personal digital assistants (PDAs) are used on an academic health sciences campus to define the level of training and support the library can provide to the students and faculty. METHOD: A Web-based questionnaire was developed. A total of 1,538 health sciences faculty and residents were sent an email message requesting participation. Data from the returned surveys were analyzed with SPSS. RESULTS: Sixty-one percent of survey respondents used PDAs. The address book, date book, and calculator were the most common uses reported for PDAs. Residents also reported a high use of drug databases on their PDAs. Most survey respondents indicated they would like to learn more about clinical resources for PDAs. CONCLUSIONS: Many opportunities exist for librarians to provide training and support for PDAs, in addition to evaluation and promotion of clinical software for PDAs.     

Setting the Students’ Bearings and Balance in Appraisal

The author, a professor at the University of Toronto, touches briefly on the extensive and rich archival literature that supports the teaching of macroappraisal, but notes that this is not the only educational material she offers her students when teaching appraisal theory. She discusses the usefulness to archivists of literature from the fields of ethnography, organizational knowing, records in history, personal documentary behaviour, memory, and communications, noting that the use of texts from these fields can encourage students to reflect on their own presumptions and to develop a taste for the wide reading and research that must support appraisal.     

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.