Determinants and outcomes of advanced practice nurses’ leadership behaviours: an AI-aided mixed-methods systematic review protocol
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".