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A computational validation for the health concept maturity levels questionnaire.

Arthur Trognon1,2,3, Islem Habibi2, Hamza Altakroury2

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Summary

This study introduces a new computational method to validate health concept maturity, using machine learning to analyze expert speech and a questionnaire. The approach shows promise for assessing health innovations but requires refinement for certain factors.

Keywords:
computational psychologycomputational psychometricsconcept maturity level healthmedical technologypsychological validationuser-centered research

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Area of Science:

  • Healthcare Innovation
  • Health Informatics
  • Psychometrics

Background:

  • The healthcare market demands user-centered design integration.
  • Living Labs facilitate co-creation and evaluation of health innovations.
  • Assessing health concept maturity is crucial for product and service development.

Purpose of the Study:

  • To develop and validate a Health Concept Maturity Levels (CMLH) questionnaire.
  • To introduce computational semantic validity using machine learning for health concepts.
  • To evaluate the CMLH questionnaire's psychometric properties.

Main Methods:

  • Developed the 178-item CMLH questionnaire.
  • Annotated expert speech acts related to Health Concept Maturity Levels.
  • Applied CatBoost and neural networks for computational semantic validation.
  • Assessed model performance in identifying CMLH factors from text.

Main Results:

  • Models trained with true factors outperformed those with random factors in identifying CMLH criteria.
  • Machine learning models demonstrated ability to discern factors from speech acts.
  • The CMLH questionnaire showed evidence of convergent and content validity.
  • Overlaps between 'Programmatic' and 'Need' factors were identified.

Conclusions:

  • Computational semantic validity offers a novel approach to psychometric validation in health innovation.
  • The CMLH questionnaire is a validated tool for assessing health concept maturity.
  • Further refinement of the CMLH model is needed to address factor overlaps.
  • The computational method shows potential for validating other psychometric tools.