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How to design a Q-sample: A seven-step approach based on interview data.

Nana Jedlicska1,2, Sabrina Lichtenberg1, Pascal O Berberat1

  • 1Technical University of Munich, TUM School of Medicine & Health, Department Clinical Medicine, TUM Medical Education Center, Munich, Germany.

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Summary
This summary is machine-generated.

This study introduces a seven-step method for designing Q-samples, crucial for Q-methodology research in medical education. This systematic approach ensures diverse viewpoints are captured effectively.

Keywords:
Q-methodologydesigning Q-sampleediting Q-samplemeasuring subjectivityphysicians’ role expectations

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Area of Science:

  • Medical Education Research
  • Qualitative and Quantitative Research Methods
  • Subjectivity in Healthcare Professionals

Background:

  • Medical education research increasingly focuses on healthcare professionals' viewpoints.
  • Q-methodology (Q) offers a robust approach to exploring subjectivity.
  • Effective Q-study design hinges on a well-developed Q-sample.

Purpose of the Study:

  • To address the gap in literature regarding Q-sample design.
  • To present a systematic, seven-step approach for Q-sample development.
  • To guide researchers in capturing diverse perspectives in medical education.

Main Methods:

  • Development of a seven-step Q-sample design approach.
  • Utilizing interview data from a prior qualitative study.
  • Employing a mapping technique for coverage and balance.
  • Focusing on editing and preserving participant language.

Main Results:

  • A defined seven-step methodology for Q-sample design.
  • Demonstration of translating interview data into a balanced Q-sample.
  • Guidelines for editing Q-samples to maintain participant voice.
  • Comprehensive criteria and practical recommendations for Q-sample selection.

Conclusions:

  • The proposed seven-step approach provides a systematic framework for Q-sample design.
  • This method enhances the validity and reliability of Q-studies in medical education.
  • Effective Q-sample design is essential for capturing nuanced viewpoints in healthcare research.