The Fourth Industrial Revolution and its Implications for Nursing Education
Bibliographic record
Abstract
Abstract Rapidly emerging AI technologies provide promising opportunities in many industries, including healthcare. AI health technologies (AIHTs) have gained popularity within health systems due to their ability to sort and analyze vast amounts of research evidence, clinical data, and patient information. AI enables the identification of patterns that can enhance knowledge generation and improve decision-making. As a result of these capabilities to transform various aspects of health systems, nurses will need to function in vastly different roles and care delivery models as AIHTs become more pervasive in many healthcare systems. Increasing evidence in the literature shows how AI algorithms and robots are already changing the nurse's role in healthcare delivery. The emergence of new roles and models in the nursing profession will require changes in the core competencies and educational requirements of nurses in all domains of their practice, including administration, clinical care, education, policy, and research. As researchers delve deeper into the potential impacts of AI health technologies (AIHTs) on nursing, particularly in nursing education, it is essential to consider the implications of these developments for nursing education. How will today's nurses be trained to function in an everchanging healthcare system where AI is increasingly pervasive? This article discusses the implications of AI and the changes needed in nursing education to ensure that nurses can deliver quality care in healthcare systems as AI becomes pervasive.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".