Embracing Evidence Based Collecting in the Health and Behavioral Sciences

Objective: Evidence based collection development requires combining multiple sources of information, such as usage data, subject librarian expertise, user values and preferences, and external evidence from librarianship research. Through triangulation, librarians can build a collection that is responsive, balanced, and relevant to address both established and emerging curricular trends. In the field of health and behavioral studies, growth in interprofessional education supports collaborative collecting practices. In 2014-2015, health and behavioral studies librarians at James Madison University (JMU) piloted a collapsed monographic selection project to facilitate better interdisciplinary collecting. This project discusses an evidence-based approach to evaluating and updating approval profiles for collaborative monographic purchasing and how the pilot affects collecting practices. Methods: Subject librarians reviewed the literature regarding approval profile performance to determine an objective benchmark. Next, they reviewed the accepted and rejected approval titles from 2012-2015 to compare performance to recommended standards. Then, they retrieved the circulation and download statistics for approval items purchased in 2012-2013 to review actual usage of accepted titles. Results: The literature indicated that approval profiles should have around a 10% rejection rate. At JMU, on average, 25% of books were rejected across disciplines and across each year. Psychology and Health Sciences had the largest areas of book rejection in comparison to Communication Sciences & Disorders, Kinesiology, Nursing, and Social Work. Of the 507 titles selected in 2012-2013, 42% circulated or were downloaded by users. Conclusion: Assessment of the approval profile found that the profile did not save librarians time with accurate collecting nor clearly meet user needs. When working in a collaborative model, librarians accept and reject titles across a broader array of LC classifications than when previously working in siloed approval profiles. The librarians applied what they learned from these disparate data sources to develop a single approval profile for more efficient collecting