Automating the Identification of Feedback Quality Criteria and the CanMEDS Roles in Written Feedback Comments Using Natural Language Processing.

ABSTRACT:

Introduction

Manually analysing the quality of large amounts of written feedback comments is time-consuming and demands extensive resources and human effort. Therefore, this study aimed to explore whether a state-of-the-art large language model (LLM) could be fine-tuned to identify the presence of four literature-derived feedback quality criteria (performance, judgment, elaboration and improvement) and the seven CanMEDS roles (Medical Expert, Communicator, Collaborator, Leader, Health Advocate, Scholar and Professional) in written feedback comments.

Methods

A set of 2,349 labelled feedback comments of five healthcare educational programs in Flanders (Belgium) (specialistic medicine, general practice, midwifery, speech therapy and occupational ther

SUBMITTER: Van Ostaeyen S 

PROVIDER: S-EPMC10742245 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature