Clustering of the Iranian Asthma and allergy specialists' clinical information-seeking behavior by Neural network analysis

Document Type : Original Article

Author

Department of Knowledge and Information Science, Islamic Azad University, Research and Science Branch, Tehran

10.22054/dcm.2020.56672.1011

Abstract

Introduction: The aim of this study was to determine the information retrieval and information therapy behavior of Asthma and allergy specialists in the country, based on cohonen self-organized neural network model.
Methods: The methodology of the present study, which is an applied study in terms of purpose, has been done by descriptive-survey method using neural network technique. The tool of this research is a researcher-made questionnaire that was distributed among a sample of people in the community (149 people). After collecting the data, the neural network was selected for data clustering and using MATLAB software, Asthma and allergy specialists were clustered based on the main components of the research. Then, by removing each of the main sub-components of the research, the most effective and least effective option in their information-seeking behavior in working with information resources in this specialized field was determined.
Results: The most effective component in clustering information barriers, was "lack of time due to workload" and the least was "distance of libraries and information centers". About information retrieval skills, the most effective component is " knowing what keywords to use when searching the Internet, and to be familiar with synonyms and terms related to the information I need."
Conclusion: By studying the clustering of information behaviors resulting from the information needs of Asthma and allergy specialists, their needs are recognized, and this is one of the measures that provides the basis for effective research, appropriate findings and ,consequently, informational decision-making for those involved in this field.

Keywords



Articles in Press, Accepted Manuscript
Available Online from 20 December 2020
  • Receive Date: 05 November 2020
  • Accept Date: 06 November 2020