A model for training the new bioinformationist

J Med Libr Assoc. 2004 Apr;92(2):188-95.

Abstract

Objectives: The objectives were to examine the effectiveness of a curriculum designed to increase bioinformatics competencies of librarians with diverse backgrounds and to identify demographic factors that may affect the learning process.

Methods: Sixteen professional staff participated in a 12-week training course consisting of 5 distinct modules: molecular biology, genetic analysis, biotechnology, research literature, and databases. Participants completed a 120-question pretest and an 88-question posttest designed to evaluate the effectiveness of the training.

Results: Training was deemed successful as all participants scored higher on the posttest than the pretest. Data analysis was conducted in relation to participant background. Holding a biology degree did not seem to affect posttest results. Years of experience, however, had an impact on final scores in the databases section, as senior team members had greater difficulty learning the material.

Discussion: As the need for specialized information in the area of molecular biology and genetics becomes more central for the effectiveness of organizations, it is crucial for libraries to quickly align with those needs by having a clear vision for increasing the skills and competencies of their staff in this subject area. This paper describes an effective model for learning that was developed and tested by the Eskind Biomedical Library.

MeSH terms

  • Biotechnology / education
  • Computational Biology / education
  • Education, Continuing / methods*
  • Education, Continuing / organization & administration
  • Female
  • Genetics / education
  • Humans
  • Inservice Training / methods*
  • Inservice Training / organization & administration
  • Librarians
  • Libraries, Medical*
  • Library Science / education*
  • Male
  • Medical Informatics / education*
  • Models, Educational*
  • Molecular Biology / education
  • Organizational Objectives
  • Tennessee
  • Time Factors
  • Workforce