Structures of Pubmed and Embase databases with NISOstandard of the thesauri to assessment of these databases\'indexing methods

• Main Authors: S Khani, MR Alibeig, H Haghani
• Format: Article
• Language: fas
• Published: Iran University of Medical Sciences 2007-04-01
• Series: مدیریت سلامت
• Subjects: MeSH; Emtree; Thesauri assessment; Pubmed; Embase; Indexing methods.
• Online Access: http://jha.iums.ac.ir/article-1-70-en.html

Introduction: According to mortality rates in Iran, cardiovascular diseases, neoplasms, perinatal mortality, and respiratory tract diseases were top rate mortality in 2003(1382). To reduce mortality rate, Iranian medical community need to know more about recent therapeutic regimens. Two main medical databases are Pubmed and Embase. Researching Pubmed and Embase indexing methods and comparing MeSH with Emtree help users to do more successful search in these databases. Consequently, designers of national medical information database in Iran may construct a model for updating indexing methods and thesaurus. This study aimed at comparing indexing methods in Pubmed and Embase. Methods: This was an applied descriptive - analytical research. Research population was all of the descriptors in MeSH and Emtree and indexed articles from Pubmed and Embase about four selected fields. In the last 3 months in 2006, all of the descriptors of selected fields were extracted through a structured search strategy. Then needed data was extracted from 6381 descriptors and 3358 articles. For collecting data we used a checklist and a questionnaire. Nine factors (including phrased descriptors versus single word descriptors, number of words in phrased descriptor, descriptor on adjectives and substantives format versus prepositions and conjunctives format, transforming versus non-transforming descriptors, using different quotation sign in descriptor structure, using abbreviations and commencer as descriptor, using definitions in descriptors, descriptors with explanations, and providing comments) selected from standard and analyzed in thesauri. Data were analyzed by SPSS using t-test and z test. Results: Emtree in six factors, and MeSH in four factors are more similar to standards. Pubmed articles are indexed with average number of 21-30 indexing terms. Embase uses average number 31-40 indexing terms for each article. Conclusions: Emtree structure is more suitable for modeling. Embase indexing method is assignmentive and derivative indexing and does it specific and more exhaustive. Pubmed indexing method is derivative and exhaustive indexing.