Frag' FlorenceEvidenz. Klar. Anwendbar.
Uhr 7/8Sources Journal Tree
Easy Demo

Lokaler Crossref-Datenbestand · journal-article

Avoiding the “Desert of the Real”: Preserving Evidence‐Based Nursing in the Age of AI

Katie A. Azama, Jessica Nishikawa

Journal of Advanced Nursing · 2026

Vollständiger Abstract

Worum geht es in dieser Arbeit?

ABSTRACT Aim A discussion of the implications of generative artificial intelligence (AI) evidence synthesis for evidence‐based practice (EBP) among nurses responsible for primary literature appraisal, guideline development, and evidence translation into clinical policy. Design Discursive paper. Data Sources Drawing on Baudrillard's (1994) theory of simulacra and simulation, we introduce epistemic distance to describe the growing separation between nurses and primary research evidence when knowledge is mediated through generative‐AI summaries. A hypothetical illustrative case depicts how a generative‐AI literature synthesis containing fabricated citations was incorporated into institutional policy undetected, illustrating how such outputs may function as simulacra that obscure methodological nuance and limit the appropriate application of evidence to complex patient contexts. Implications for Nursing Preserving evidence‐appraisal skills and engagement with primary resources is essential to maintaining the integrity of EBP in the age of AI, particularly for those in evidence‐appraisal‐facing roles who shape the institutional evidence base upon which other nurses rely. Conclusion Overreliance on generative AI outputs may increase epistemic distance, shifting nurses from active engagement with primary evidence towards interaction with simulacra of that evidence, with implications for critical appraisal, professional autonomy, and person‐centred care. Emerging empirical research on fabricated citations in the health sciences literature and on nurses' perceptions of AI further highlights concerns related to accuracy and overreliance. Impact For nurses in evidence‐appraisal‐facing roles, safeguarding evidence‐appraisal literacy requires maintaining manual EBP competencies, verifying AI‐generated outputs against primary sources, and ensuring transparency in AI‐assisted evidence synthesis; without these safeguards, epistemic distance from primary evidence may compromise the institutional policies and processes that shape downstream nursing practice.

Bibliografischer Nachweis

Publikationsdaten

Autor:innen
Katie A. Azama, Jessica Nishikawa
Quelle
Journal of Advanced Nursing
Publikation
2026-01-01
Band / Ausgabe
Nicht angegeben
Seiten
Nicht angegeben
ISSN / ISBN
0309-2402, 1365-2648
Zitationen
0 laut Crossref
Referenzen
0 hinterlegt

Zitieren

Zitierfähiger Nachweis

Katie A. Azama, Jessica Nishikawa (2026). Avoiding the “Desert of the Real”: Preserving Evidence‐Based Nursing in the Age of AI. Journal of Advanced Nursing. https://doi.org/10.1111/jan.70732
RIS BibTeX CSL-JSON

Kontext

Themen, Förderung und Nutzung

Lizenzhinweise: Lizenz 1 · Lizenz 2