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Related Experiment Video

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Modulation of the Neurophysiological Response to Fearful and Stressful Stimuli Through Repetitive Religious Chanting
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A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.

Joshua Glauser1,2, Brian Connolly1, Paul Nash3

  • 1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

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Summary

Healthcare chaplains can identify patients with religious struggles by analyzing chapel notebook entries. Natural language processing accurately detects negative religious coping, aiding in patient care prioritization.

Keywords:
Prayerchaplaincymachine learningnatural language processingspiritual struggle

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Area of Science:

  • Clinical psychology
  • Computational linguistics
  • Pastoral care

Background:

  • Religious or spiritual struggles significantly impact patient health outcomes, affecting both mental and physical well-being.
  • Identifying patients experiencing religious struggles is a challenge for healthcare chaplains.
  • Chapel notebooks offer a potential, yet underutilized, resource for triaging chaplaincy services.

Purpose of the Study:

  • To investigate the feasibility of using natural language processing (NLP) to identify religious struggle in chapel notebook entries.
  • To determine if NLP methods can classify entries indicative of religious struggle with accuracy comparable to human assessment.

Main Methods:

  • A dataset of chapel notebook entries was curated and manually classified to identify instances of religious struggle.
  • Natural language processing (NLP) techniques were applied to automatically classify notebook entries.
  • The accuracy of the NLP classification was compared against human classification.

Main Results:

  • Religious struggle can be identified in chapel notebook entries through indicators of negative religious coping, such as feelings of anger or abandonment toward God.
  • NLP techniques demonstrated the ability to automatically classify entries reflecting religious struggle.
  • The accuracy of the automated NLP classification was comparable to that of human classifiers.

Conclusions:

  • NLP offers a viable and accurate method for identifying religious struggle in chapel notebook entries.
  • This approach has practical applications for healthcare chaplains in triaging pastoral care efforts.
  • Automated analysis of chapel notebooks can enhance the efficiency and effectiveness of spiritual support in healthcare settings.