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Updated: May 25, 2025
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Concept analysis of health research translation nomenclature
Lucylynn Lizarondo1, Zoe Jordan2, Ecushla Linedale3
1JBI, School of Public Health, University of Adelaide, North Adelaide, South Australia, Australia lucylynn.lizarondo@adelaide.edu.au.
Introduction:
Translating health research into clinical practice is a complex process aimed at enhancing healthcare quality and patient outcomes. The terminology surrounding this process is varied and often used interchangeably, leading to minimal consensus on the activities encompassed by each term.
Objectives:
This study aims to examine existing taxonomies and websites for operational definitions related to health research translation, culminating in a comprehensive synopsis of terms specific to this field.
Design:
In 2019, a literature search was conducted using databases such as PubMed and CINAHL, along with relevant government and non-governmental organisation (NGO) websites, including grey literature. The search focused on English-language publications defining health research translation from 2000 onward and was updated in 2023. One author conducted the search, employing a mix of free-text and database-specific terms. Two authors independently evaluated the results for inclusion. Relevant data were extracted to aid in sorting and prioritising terminology based on frequency. A concept analysis approach, developed by Foley and Davis and informed by Rodgers' seven phases, was used to map the nomenclature.
Results:
A total of 51 papers were analysed, revealing that the most frequently used terms for health research translation were knowledge translation (KT), implementation and translational research. Both evidence-based healthcare and KT describe the process of integrating evidence into practice, positioning them as analogous. Two major domains were identified: practice and science, with practice-related language further categorised into people-focused, process-focused and outcome-focused.
Conclusions:
This paper presents a conceptual nomenclature map that serves as a foundation for developing a consensus-driven ontology for health research translation. The framework highlights how language can be categorised into common domains, fostering meaningful communication across diverse groups and entities.
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