This Masterclass supports participants in designing robust qualitative studies that are theoretically grounded, methodologically rigorous and relevant to clinical pharmacy practice. It focuses on the foundations of qualitative excellence, including interpretive approaches, reflexivity, theoretical frameworks, purposive sampling, data saturation and trustworthiness. Particular attention will be given to the role of the researcher as the primary instrument in qualitative inquiry and to the importance of reflexivity and positionality when interpreting patient and professional experiences.

The Masterclass also addresses the expanding role of Artificial Intelligence (AI) in qualitative data analysis. AI-assisted Computer-Assisted Qualitative Data Analysis Software (CAQDAS) and no-code analytical platforms offer new opportunities to support coding, theme development and pattern recognition across large qualitative datasets. However, these tools must be used transparently, ethically and critically, ensuring that clinical meaning, contextual interpretation and researcher judgement are central.

Participants will learn how to design qualitative studies, develop theoretically-grounded interview guides, apply strategies to enhance trustworthiness, use AI-supported tools for thematic analysis, and critically appraise qualitative and AI-augmented methodologies for publication in high-quality journals.

The aim of this Masterclass is to guide participants through the design of theoretically-grounded and methodologically rigorous qualitative studies, while demonstrating how AI-assisted analytical tools can be used responsibly to support the interpretation of large-scale qualitative datasets in clinical pharmacy research.