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Area of Science:

  • Medical Education
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Reflective writing is a pedagogical tool used in medical education to foster inter-professional skills, leadership, and career development.
  • These non-technical skills are crucial for trainee success and clinical practice, but extracting actionable feedback from reflective texts is challenging.
  • The subjective nature of reflective writing presents a significant hurdle for automated analysis.

Purpose of the Study:

  • To develop and evaluate a Natural Language Processing (NLP) pipeline for analyzing medical trainees' reflective writing.
  • To identify common challenges faced by Acting Interns through automated analysis of their reflective texts.
  • To provide medical educators with tools to improve curriculum and support trainees during their transition.

Main Methods:

  • A Natural Language Processing pipeline was implemented, incorporating a seeded Term Frequency-Inverse Document Frequency (TF-IDF) matrix.
  • The pipeline included sentence-level summarization, sentiment analysis, and clustering techniques to organize textual data.
  • This approach aimed to group sentences, facilitating the identification of recurring themes and challenges.

Main Results:

  • The NLP pipeline successfully identified both known and previously unrecognized challenges in trainees' reflective writing.
  • Specific issues identified include difficulties accessing the electronic health system and adapting to specialty-specific differences.
  • The automated analysis provided organized topics that can highlight areas for curriculum improvement.

Conclusions:

  • Automated analysis of reflective writing using NLP is feasible despite its inherent subjectivity.
  • The developed pipeline effectively aids medical educators in understanding and addressing trainee challenges.
  • This method offers a scalable approach to enhance medical training and support during critical transitional periods.