Indexing consistency in MEDLINE

The quality of indexing of periodicals in a bibliographic data base cannot be measured directly, as there is no one "correct" way to index an item. However, consistency can be used to measure the reliability of indexing. To measure consistency in MEDLINE, 760 twice-indexed articles from 42 periodical issues were identified in the data base, and their indexing compared. Consistency, expressed as a percentage, was measured using Hooper's equation. Overall, checktags had the highest consistency. Medical Subject Headings (MeSH) and subheadings were applied more consistently to central concepts than to peripheral points. When subheadings were added to a main heading, consistency was lowered. "Floating" subheadings were more consistent than were attached subheadings. Indexing consistency was not affected by journal indexing priority, language, or length of the article. Terms from MeSH Tree Structure categories A, B, and D appeared more often than expected in the high-consistency articles; whereas terms from categories E, F, H, and N appeared more often than expected in the low-consistency articles. MEDLINE, with its excellent controlled vocabulary, exemplary quality control, and highly trained indexers, probably represents the state of the art in manually indexed data bases.     

A regional cooperative acquisition program for monographs.

A cooperative acquisition program for monographs for the twelve resource libraries in Region IX of the Regional Medical Library Network is described. Each of the participating libraries has agreed to purchase all books of an assigned publisher which fall within a prescribed subject-format profile. It is hoped that this will help to reduce unnecessary duplication and contribute toward the development of resources in the region.     

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.     

Can constrained relevance feedback and display strategies help users retrieve items on mobile devices?

Searching online information resources using mobile devices is affected by small screens which can display only a fraction of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ) and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking that attempts to maximize information gain from the user’s choices and the top-D ranked documents. Experimental results indicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective. Extended version of “Evaluating Relevance Feedback Algorithms for Searching on Small Displays, ” Vishwa Vinay, Ingemar J. Cox, Natasa Milic-Frayling, Ken Wood published in the proceedings of ECIR 2005, David E. Losada, Juan M. Fernández-Luna (Eds.), Springer 2005, ISBN 3-540-25295-9