An Automatic Method for Classifying Medical Researchers into Domain Specific Subgroups

Autor/in	Cecchetti, Alfred A.
Titel	An Automatic Method for Classifying Medical Researchers into Domain Specific Subgroups
Quelle	(2009), (163 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, University of Pittsburgh
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-1093-9541-9
Schlagwörter	Hochschulschrift; Dissertation; Medical Research; Medical Schools; Medicine; Classification; Researchers; Methods; Intellectual Disciplines; Content Analysis; Automation; Computational Linguistics; Expertise; Interrater Reliability; Comparative Analysis; Medical School Faculty; Bibliometrics; Outcome Measures; Information Systems; Information Science; Library Services; Pennsylvania + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Medizinische Ausbildung; Medizin; Classification system; Klassifikation; Klassifikationssystem; Researcher; Forscher; Method; Methode; Geisteswissenschaften; Inhaltsanalyse; Linguistics; Computerlinguistik; Expert appraisal; Interrater-Reliabilität; Bibliometrie; Informationswissenschaft; Bibliotheksarbeit
Abstract	Objective: This dissertation developed an automatic classification procedure, as an example of a novel tool for an informationist, which extracts information from published abstracts, classifies abstracts into their "fields of study," and then determines the researcher's "field of study" and "level of activity." Method: This dissertation compared a domain expert's method of classification and an automatic classification procedure on a random sample of 101 medical researchers (derived from a potential list of 305 medical researchers) and their associated abstracts. Design: The study design is a retrospective, cross-sectional, inter-rater agreement study, designed to compare two classification methods (i.e., automatic classification procedure and domain expert). The study population consists of University of Pittsburgh, School of Medicine, Department of Medicine (DOM) professionals who (1) have published at least one article listed in PubMed[R] as first or last author and/or (2) are the primary investigator for at least one grant listed in CRISP. Main outcome measures. Three outcome measures were derived from the domain expert's versus automatic categorization procedure: (1) an abstract's "field of study," (2) a researcher's "field of study" and (3) a researcher's "level of activity and field of study." Results: Kappa showed moderate agreement between automatic and domain expert classification for the abstracts' "field of study" (Kappa = 0.535, n = 504, p less than 0.000). Kappa showed moderate agreement between automatic and domain expert classification of the researcher's "field of study" (Kappa = 0.535, n = 101, p less than 0.000). Kappa showed good agreement between automatic and domain expert classification of the researcher's "level of activity and field of study" (Kappa = 0.634, n = 101, p less than 0.000). Conclusion. The study suggests that an automatic library classification procedure can provide rapid classification of medical research abstracts into their "fields of study." The classification procedure can also process multiple abstracts' "fields of study" and classify their associated medical researchers into their "field of study" and "level of activity and field of study." The classification procedure, used as a tool by an informationist, can be used as the basis for new services. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Update	2017/4/10