Nurse Leadership and Artificial Intelligence Integration in Nursing Workforce Management: A Scoping Review
Bibliographic record
Abstract
AIM: To systematically map evidence on the application of AI systems in nursing workforce management, with a targeted focus on the role of nurse leaders. DESIGN: A scoping review. DATA SOURCES: A comprehensive literature search was conducted across six databases: CINAHL, IEEE Xplore, MEDLINE/PubMed, PsycINFO, Scopus, and Web of Science. Studies published in English between January 2015 and December 2024 were included. REVIEW METHODS: Studies that focused on AI in the context of nursing leadership or workforce management were included, while those examining AI in healthcare but without a specific focus on nursing leadership/management were excluded. RESULTS: A total of 1014 articles were retrieved, and 12 were included in this review. Eleven articles were published between 2022 and 2024. The findings show that AI systems in nursing management have been applied in several domains, including workforce planning, nursing safety, and staff prediction models. Although studies highlight the positive optimising potential of AI systems, others underscore the ethical implications of AI with respect to nursing leadership and management, particularly regarding discriminatory stereotypes in AI-generated nurse imagery and the critical role of nurse leaders in ethical AI integration in care. Only one study identified important barriers to AI integration, underlining the need for enhanced AI training for nurse managers. CONCLUSIONS: Findings suggests that the application of AI systems in nursing leadership/management is in its early phases, with limited engagement of nurses in innovating and implementing AI-enabled systems. A substantial problem related to AI adoption remains-AI integration hinges on addressing the readiness and engagement levels of nurse leaders early on in the process of AI systems' innovation. To promote AI integration, AI competency, trust, and optimisation in healthcare, developing a basic working understanding of AI together with a culture of multidisciplinary AI development teams that include nurses are potentially proactive strategies. REPORTING METHOD: This study adhered to the PRISMA-ScR guideline. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".