Navigating the integration of artificial intelligence in Nursing: Opportunities, challenges, and strategic actions
Bibliographic record
Abstract
The advent of artificial intelligence (AI) in recent years has brought about transformative changes across various sectors, including healthcare. In nursing practice, education, and research, AI has the potential to revolutionize traditional methodologies, enhance learning experiences, and improve patient outcomes. Integrating AI tools and techniques can provide clinicians with smarter clinical solutions and nursing students with more robust and interactive learning environments, while also advancing research capabilities in the field. Despite the promising prospects, the incorporation of AI into nursing practice, education, and research presents several challenges. Firstly, there is a concern about the potential displacement of human roles in nursing due to automation, which may affect the human-centric nature of nursing care. Secondly, there are issues related to the lag in AI competency among nurses. Many current nursing curricula do not include comprehensive AI training, leading to a lack of preparedness in utilizing these technologies effectively. Lastly, the ethical implications of AI in healthcare, such as data privacy, patient consent, and the potential for biased algorithms, need to be meticulously addressed. To harness the full potential of AI in nursing practice, education, and research, several strategic actions including reinvesting in humanistic practice, revising core competencies and curriculum, and developing new ethical guidelines.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".