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Developing a classification system and algorithm to track community-engaged research using IRB protocols at a large

Emily B Zimmerman1, Sarah E Raskin2, Brian Ferrell3

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This study developed an automated method to identify and categorize community-engaged research (CEnR) using deep learning algorithms. This approach enhances tracking of research partnerships and engagement levels through administrative data.

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Area of Science:

  • Health Services Research
  • Public Health
  • Research Administration

Background:

  • Community-engaged research (CEnR) is a recognized methodology.
  • Tracking CEnR longitudinally and systematically is challenging.
  • Existing methods for identifying CEnR lack automation and standardization.

Purpose of the Study:

  • To pilot a systematic and automated method for identifying and categorizing CEnR.
  • To facilitate longitudinal tracking of CEnR using administrative data.
  • To develop and validate a deep-learning algorithm for CEnR classification.

Main Methods:

  • Inductive analysis and manual coding of Institutional Review Board (IRB) protocols.
  • Development of five partnership categories: Non-CEnR, Instrumental, Academic-led, Cooperative, and Reciprocal.
  • Training a deep-learning algorithm with natural language processing (NLP) on coded protocols.
  • Comparison of algorithm results with investigator-recorded data from IRB applications.

Main Results:

  • The developed algorithm shows potential for higher categorization of studies as CEnR compared to investigator data.
  • The algorithm may identify studies with higher levels of community engagement.
  • Preliminary results indicate feasibility of automated CEnR identification.

Conclusions:

  • Automated identification and categorization of CEnR using administrative data is feasible.
  • This approach can support university strategic planning and progress tracking.
  • Standardized reporting of CEnR trends can be implemented by research institutions.