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.     

Character N-Gram Tokenization for European Language Text Retrieval

The Cross-Language Evaluation Forum has encouraged research in text retrieval methods for numerous European languages and has developed durable test suites that allow language-specific techniques to be investigated and compared. The labor associated with crafting a retrieval system that takes advantage of sophisticated linguistic methods is daunting. We examine whether language-neutral methods can achieve accuracy comparable to language-specific methods with less concomitant software complexity. Using the CLEF 2002 test set we demonstrate empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages. We show that n = 4 is a good choice for those languages, and document the increased storage and time requirements of the technique. We report on the benefits of and challenges posed by n-grams, and explain peculiarities attendant to bilingual retrieval. Our findings demonstrate clearly that accuracy using n-gram indexing rivals or exceeds accuracy using unnormalized words, for both monolingual and bilingual retrieval.     

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.     

Validating the Willingness to Self-Censor Scale: Individual Differences in the Effect of the Climate of Opinion on Opinion Expression

Hayes, Glynn, and Shanahan (2005) defined self-censorship asthe withholding of one’s opinion around an audience perceivedto disagree with that opinion. They argued that people differin their willingness to self-censor and introduced an 8-itemself-report instrument, the Willingness to Self-Censor scale,to measure this individual difference. The results of an experimentalstudy presented here provide further evidence of the constructvalidity of the scale. Each participant in the study was presentedwith a hypothetical scenario that contained information suggestinga group of people the participant was conversing with abouta controversial topic held opinions that were either uniformlysimilar to or different from the participant’s own opinion.Four weeks prior, each participant had responded to the Willingnessto Self-Censor scale and a measure of dispositional shyness.As expected, the manipulation of the climate of opinion affectedwillingness to express an opinion to the group, but more soamong those who scored relatively high on the Willingness toSelf-Censor scale. These results support the notion that somepeople rely on information about the climate of opinion moreso than do others when they decide whether or not to voice theiropinion publicly, and they suggest that the Willingness to Self-Censorscale measures this individual difference.     

Online journals' impact on the citation patterns of medical faculty.

PURPOSE: The purpose was to determine the impact of online journals on the citation patterns of medical faculty. This study looked at whether researchers were more likely to limit the resources they consulted and cited to those journals available online rather than those only in print. SETTING: Faculty publications from the college of medicine at a large urban university were examined for this study. The faculty publications from a regional medical college of the same university were also examined in the study. The number of online journals available for faculty, staff, and students at this institution has increased from an initial core of 15 online journals in 1998 to over 11,000 online journals in 2004. METHODOLOGY: Searches by author affiliation were performed in the Web of Science to find all articles written by faculty members in the college of medicine at the selected institution. Searches were conducted for the following years: 1993, 1996, 1999, and 2002. Cited references from each faculty-authored article were recorded, and the corresponding cited journals were coded into four categories based on their availability at the institution in this study: print only, print and online, online only, and not owned. Results were analyzed using SPSS. RESULTS: The number of journals cited per year continued to increase from 1993 to 2002. The results did not indicate that researchers were more likely to cite online journals or were less likely to cite journals only in print. At the regional location where the number of print-only journals was minimal, use of the print-only journals did decrease in 2002, although not significantly. CONCLUSION/DISCUSSION: It is possible that electronic access to information (i.e., online databases) has had a positive impact on the number of articles faculty will cite. Results of this study suggest, at this point, that faculty are still accessing the print-only collection, at least for research purposes, and are therefore not sacrificing quality for convenience.     

Automated Classification to Improve the Efficiency of Weeding Library Collections

Previous studies have shown that weeding a library collection benefits patrons and increases circulation rates. However, the time required to review the collection and make weeding decisions presents a formidable obstacle. This study empirically evaluated methods for automatically classifying weeding candidates. A data set containing 80,346 items from a large-scale weeding project running from 2011 to 2014 at Wesleyan University was used to train six machine learning classifiers to predict a weeding decision of either ‘Keep’ or ‘Weed’ for each candidate. The study found statistically significant agreement (p?=?0.001) between classifier predictions and librarian judgments for all classifier types. The naive Bayes and linear support vector machine classifiers had the highest recall (fraction of items weeded by librarians that were identified by the algorithm), while the k-nearest-neighbor classifier had the highest precision (fraction of recommended candidates that librarians had chosen to weed). The variables found to be most relevant were: librarian and faculty votes for retention, item age, and the presence of copies in other libraries.