Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques.

Journal: Frontiers in public health
Published Date: Oct 31, 2023

Abstract

INTRODUCTION: Machine-assisted topic analysis (MATA) uses artificial intelligence methods to help qualitative researchers analyze large datasets. This is useful for researchers to rapidly update healthcare interventions during changing healthcare contexts, such as a pandemic. We examined the potential to support healthcare interventions by comparing MATA with "human-only" thematic analysis techniques on the same dataset (1,472 user responses from a COVID-19 behavioral intervention).

Authors

  • Lauren Towler
    School of Psychology, University of Southampton, Southampton, United Kingdom.
  • Paulina Bondaronek
    Department of Health and Social Care, Office for Health Improvement and Disparities, London, United Kingdom.
  • Trisevgeni Papakonstantinou
    Department of Health and Social Care, Office for Health Improvement and Disparities, London, United Kingdom.
  • Richard Amlôt
    Behavioural Science and Insights Unit, UK Health Security Agency, London, United Kingdom.
  • Tim Chadborn
    Department of Health and Social Care, Office for Health Improvement and Disparities, London, United Kingdom.
  • Ben Ainsworth
    Department of Psychology, University of Bath, Bath, United Kingdom.
  • Lucy Yardley
    School of Psychology, University of Southampton, Southampton, United Kingdom.

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