Identifying "real-world" initiatives for knowledge translation tools: a case study of community-based physical activity programs for persons with physical disability in Canada

Abstract

Background: ‘Real-world’ initiatives represent an important source of information for evidence-based practice; however, accessing information about initiatives is often challenging. Casebooks are an innovative knowledge translation (KT) tool for researchers, practitioners, and end-users to address ‘research-to-implementation gaps’ through sharing ‘real-world’ experiences. Several casebooks have been published; yet, they remain inconsistent in their methodological approach for identifying ‘real-world’ initiatives. Purpose: The purpose of this project is to describe and apply systematic scoping study methods for the identification of ‘real-world’ initiatives relevant for the development of KT tools. Specifically, systematic scoping study methods were developed to identify community-based physical activity (PA) programs for persons with physical disabilities across Canada. Methods: To identify PA programs, a search strategy was developed and included five distinct search approaches: (1) peer-reviewed literature databases; (2) grey literature databases; (3) customized Google search engines; (4) targeted websites; and (5) consultation with content experts. Results: Title screening and hand searching identified 478 potentially relevant PA programs. Full record review identified 72 PA programs that met KT tool criteria. The most comprehensive search approach was targeted websites, which identified 25 (35%) unique PA programs, followed by content experts (n=12; 17%). Only four (5.6%) unique PA programs were identified via custom Google searching. No PA programs were uniquely identified through peer- or grey-literature database searches. Conclusions: This study describes and applies a systematic scoping study methodology that serves as a basis for identifying and selecting ‘real-world’ initiatives that are central to the development of evidence-based KT tools.