aceCompeting 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 nurs…
Seven large language models gave internally coherent, authoritative answers on the global nursing shortage but framed it in different ways and never acknowledged the situatedness of their framing, which risks misleading nursing and policy decisions.
The authors describe the link between framing patterns and the models' development contexts only as appearing consistent, so it is not established as causal. The analysis also rests on a single standardized zero-shot prompt.
Evidence
- Peer-reviewedNursing Inquiry2026-10-01
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Truvace Impact Record TRV-2026-1567, v1: “Competing Framings: Variability in AI-Generated Health Policy Guidance and Its Implications for Global Nursing.” Truvace, 2026-10-16. /record/TRV-2026-1567 (accessed at citation time). sha256 a51deac77ea9667b…
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