Competing Framings: Variability in AI-Generated Health Policy Guidance and Its Implications for Global Nursing

Artificial intelligence systems are increasingly drawn upon to inform nursing practice, education, and health policy, often on the assumption that they produce stable, broadly consistent knowledge. This paper challenges that assumption by examining how seven widely accessible large language models, developed in distinct socio-technical and geopolitical contexts, interpret a shared global nursing challenge. Through comparative qualitative analysis of responses to a standardized zero-shot prompt on the global nursing shortage, four competing policy logics were identified, namely workforce, efficiency, equity, and mobility, with patterns that appeared consistent with aspects of the institutional environments associated with those systems' development. Each response was internally coherent and presented with substantial authority, yet none acknowledged the situatedness of its own framing. The paper argues that such outputs in nursing should be understood as situated artifacts rather than as neutral knowledge, and offers a framework of questions to guide discretion for nurses, educators, and policymakers engaging with these tools. Recognizing this plurality is necessary for safe, contextually grounded decision-making and for governance that addresses both interpretive variability and accuracy.