Background <p>Advanced practice nurses play a vital role in healthcare innovation, delivering high-quality care and improving patient outcomes. Leadership is a core competency of advanced practice nurses, empowering them to drive systemic improvements and foster collaboration. However, these master-level educated nurses often encounter challenges in assuming leadership roles, including limited recognition and competing demands on their time. The growing volume of healthcare-related research, combined with the lack of a comprehensive evidence base on the determinants and outcomes of their leadership behaviours, complicates the development of effective programmes. This protocol outlines a systematic approach to addressing these challenges, using an AI tool to efficiently manage the expanding evidence base and provide a detailed understanding of the factors influencing advanced practice nurses’ leadership behaviours.</p> Methods <p>This protocol follows the PRISMA-P 2015 guidelines to outline a systematic review investigating the determinants and outcomes of advanced practice nurses’ leadership behaviours. It employs the SPIDER tool for eligibility criteria, encompassing studies that explore advanced practice nursing leadership behaviours and their determinants and outcomes. Eligible studies include quantitative, qualitative and mixed-methods research, focusing on advanced practice nursing roles. The protocol also outlines a workflow for AI-aided title and abstract screening using ASReview LAB, incorporating multi-phase human validation to ensure accuracy and reliability. Data synthesis will utilise narrative synthesis for quantitative data and meta-aggregation for qualitative findings, integrating results through narrative weaving.</p> Discussion <p>This protocol addresses a critical gap in nursing research by systematically exploring the determinants influencing advanced practice nurses’ leadership behaviours and their outcomes. It provides evidence to inform the development of tailored programmes aimed at empowering advanced practice nurses to maximise their leadership potential. Additionally, the protocol demonstrates how AI tools can enhance systematic review efficiency while maintaining methodological rigour. The findings will not only contribute to advancing nursing practice but also highlight the transformative potential of AI in research synthesis, ensuring timely and robust evidence generation amidst the expanding volume of healthcare-related research.</p> Systematic review registration <p>PROSPERO CRD42025644174.</p>

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Determinants and outcomes of advanced practice nurses’ leadership behaviours: an AI-aided mixed-methods systematic review protocol

  • Vincent Put,
  • Hanne Kindermans,
  • Ann Van Hecke,
  • Greta G. Cummings,
  • Ellen Vlaeyen

摘要

Background

Advanced practice nurses play a vital role in healthcare innovation, delivering high-quality care and improving patient outcomes. Leadership is a core competency of advanced practice nurses, empowering them to drive systemic improvements and foster collaboration. However, these master-level educated nurses often encounter challenges in assuming leadership roles, including limited recognition and competing demands on their time. The growing volume of healthcare-related research, combined with the lack of a comprehensive evidence base on the determinants and outcomes of their leadership behaviours, complicates the development of effective programmes. This protocol outlines a systematic approach to addressing these challenges, using an AI tool to efficiently manage the expanding evidence base and provide a detailed understanding of the factors influencing advanced practice nurses’ leadership behaviours.

Methods

This protocol follows the PRISMA-P 2015 guidelines to outline a systematic review investigating the determinants and outcomes of advanced practice nurses’ leadership behaviours. It employs the SPIDER tool for eligibility criteria, encompassing studies that explore advanced practice nursing leadership behaviours and their determinants and outcomes. Eligible studies include quantitative, qualitative and mixed-methods research, focusing on advanced practice nursing roles. The protocol also outlines a workflow for AI-aided title and abstract screening using ASReview LAB, incorporating multi-phase human validation to ensure accuracy and reliability. Data synthesis will utilise narrative synthesis for quantitative data and meta-aggregation for qualitative findings, integrating results through narrative weaving.

Discussion

This protocol addresses a critical gap in nursing research by systematically exploring the determinants influencing advanced practice nurses’ leadership behaviours and their outcomes. It provides evidence to inform the development of tailored programmes aimed at empowering advanced practice nurses to maximise their leadership potential. Additionally, the protocol demonstrates how AI tools can enhance systematic review efficiency while maintaining methodological rigour. The findings will not only contribute to advancing nursing practice but also highlight the transformative potential of AI in research synthesis, ensuring timely and robust evidence generation amidst the expanding volume of healthcare-related research.

Systematic review registration

PROSPERO CRD42025644174.