Rethinking qualitative inquiry in implementation science: clarifying purpose, design, and rigor

Qualitative methods are now central to implementation science, but they are used to do different kinds of work that are not always clearly distinguished. Some studies are designed to develop contextual or conceptual understanding, while others are expected to inform near-term decisions about rollout, adaptation, or implementation strategies. When these differences remain implicit, qualitative studies may be designed and evaluated against expectations they were never intended to meet, particularly around the role of theory, openness to unanticipated findings, and what counts as rigor. In this debate paper, we argue for a more explicit way of thinking about qualitative inquiry in implementation science. Rather than treating qualitative methods as a single approach, we suggest they are being configured in different ways in response to study purpose, time and resource constraints, the state of knowledge about the phenomenon or context, and stakeholder expectations. To make these differences visible, we propose a positioning framework that locates qualitative inquiry along a continuum of three orientations: Generative, Pragmatic, and Action-oriented. Generative inquiry prioritizes contextual depth and conceptual development; Action-oriented inquiry is organized to produce timely, decision-relevant findings; Pragmatic inquiry occupies the space between these poles. We then introduce a positioning guide and an accompanying table to show how these orientations shape key aspects of qualitative design, including question formulation, the role of theory, sampling, data collection, analysis, and reporting. The contribution of this paper is a framework for describing what qualitative studies in implementation science are trying to produce and how they should be assessed. Qualitative rigor cannot be reduced to a single standard when studies are making different kinds of claims. Making methodological positioning more explicit may help reduce mismatched expectations in study design and peer review, and support more consistent judgments about the contribution of qualitative work in implementation science.