Reflective analysis in radiography research: Methodological foundations and reporting guidance

Radiography research has traditionally prioritised quantitative approaches, which are less suited to examining complex practice-based phenomena such as professional judgement, ethical tension, and relational aspects of care and education. Reflective analysis has been used to explore these issues but remains poorly defined as a research method in radiography, leading to inconsistency in application and reporting. This paper aims to establish reflective analysis as a recognised qualitative research method by defining its epistemic foundations, analytic procedures, and reporting requirements, and by distinguishing it from reflective writing for learning.

A methodological synthesis drawing on qualitative research methodology literature, reflective practice theory, and radiography scholarship was undertaken. Within the included radiography literature (n=24), reflexive thematic analysis was the most commonly used analytic approach (n=15), followed by narrative inquiry (n=4), reflective analysis (n=2), analytic autoethnography (n=2), and interpretive description (n=1). Examination of these approaches clarified key differences in data sources, analytic focus, and methodological intent, highlighting the distinct contribution of reflective analysis. Reflective analysis is defined as a qualitative method that uses systematically documented first-person professional experience as data and applies explicit analytic procedures to generate practice-facing insights. From this synthesis, a seven-stage analytic workflow, Reflective Analysis for Radiography (RAD-RA), and discipline-specific reporting guidance aligned with established qualitative standards were developed.

Reflective analysis provides a structured qualitative approach for examining complex aspects of radiography practice, education, and professional decision-making not readily captured through quantitative or multi-participant qualitative designs.

The RAD-RA framework supports rigorous, transparent, and ethically accountable reflective research, enabling radiographers to examine professional judgement and practice-based challenges while supporting consistent peer review and qualitative scholarship within the discipline. As artificial intelligence increasingly shapes clinical workflows, decision support, and professional roles in radiography, reflective analysis provides a structured qualitative method for examining how practitioners interpret, negotiate, and respond to AI-supported practice. The RAD-RA framework offers a defensible approach for analysing these emerging interactions, supporting qualitative inquiry into human-AI relationships, professional accountability, and practice implications within radiography.