This study analyzes the search behavior of Dutch‐speaking nursing students with a nonnative knowledge of English who searched for information in MEDLINE/PubMed about a specific theme in nursing. We examine whether and to what extent their search efficiency is affected by their language skills. Our task‐oriented approach focuses on three stages of the information retrieval process: need articulation, query formulation, and relevance judgment. The test participants completed a pretest questionnaire, which gave us information about their overall experience with the search system and their self‐reported computer and language skills. The students were briefly introduced to the use of PubMed and MeSH (medical subject headings) before they conducted their keyword‐driven subject search. We assessed the search results in terms of recall and precision, and also analyzed the search process. After the search task, a satisfaction survey and a language test were completed. We conclude that language skills have an impact on the search results. We hypothesize that language support might improve the efficiency of searches conducted by Dutch‐speaking users of PubMed.
With the explosion of information available on the Web, finding specific medical information in an efficient way has become a considerable challenge. PubMed/MEDLINE offers an alternative to freetext searching on the web, allowing searchers to do a keyword-based search using Medical Subject Headings. However, finding relevant information within a limited time frame remains a difficult task.The current study is based on an error analysis of data from a retrieval experiment conducted at the nursing departments of two Belgian universities and a British university. We identified the main difficulties in query formulation and relevance judgment and compared the profiles of the best and worst performers in the test.For the analysis, a query collection was built from the queries submitted by our test participants. The queries in this collection are all aimed at finding the same specific information in PubMed, which allowed us to identify what exactly went wrong in the query formulation step. Another crucial aspect for efficient information retrieval is relevance judgment. Differences between potential and actual recall of each query offered indications of the extent to which participants overlooked relevant citations.The test participants were divided into "worst", "average" and "best" performers based on the number of relevant citations they selected: zero, one or two and three or more, respectively. We tried to find out what the differences in background and in search behavior were between these three groups.
Despite the legislative efforts to improve the readability of patient information, different surveys have shown that respondents still feel distressed by reading the patient information leaflet. One of the main sources of distress is the use of scientific terminology. In order to assess the scale of the problem, we collected a Dutch-English parallel corpus of European Public Assessment Reports (EPARs) which was annotated by two linguists. This corpus was used to evaluate and train an automatic approach to scientific term detection.As an alternative to the dictionary-based approaches, which suffer from low coverage, we present a classification-based approach relying on a wide variety of information sources, such as local context and lexical information, termhood and unithood information, cognate identification and morphological information. We present the experimental findings and show some first results of the automatic replacement of the detected terms by their popular counterpart.
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