Exploring the impact of socio-cognitive factors on adherence to asthma medication using traditional mixed methods and machine learning - Ljevar, Vanja (2022)
Asthma self-management and regular use of medication are crucial in preventing asthma attacks, which can be fatal. However, existing research usually does not take into consideration the impact that perceptions about asthma, of both patients and the general public have on adherence to treatment. Both methodological and empirical contributions are made, combining the approaches from data science and psychology. The thesis proposes new forms of triangulation of alternative research methods; introduces a novel process of identifying perceptions in text, as well as the use of Grouped Model Class Reliance, which is capable of assessing the most predictive feature group while accounting for non-linear relationships. Empirical contributions are reflected in identification of the leading perceptions, that include stigmatization and its underlying mechanisms, as well as the perceived sense of community.