Current trends and future implications in the utilization of ChatGPT in nursing: A rapid review
Bibliographic record
Abstract
Background: The past decade has witnessed a surge in the development of artificial intelligence (AI)-based technology systems for healthcare. Launched in November 2022, ChatGPT (Generative Pre-trained Transformer), an AI-based Chatbot, is being utilized in nursing education, research and practice. However, little is known about its pattern of usage, which prompted this study. Objective: To provide a concise overview of the existing literature on the application of ChatGPT in nursing education, practice and research. Methods: A rapid review based on the Cochrane methodology was applied to synthesize existing literature. We conducted systematic searches in several databases, including CINAHL, Ovid Medline, Embase, Web of Science, Scopus, Education Search Complete, ERIC, and Cochrane CENTRAL, to ensure no publications were missed. All types of primary and secondary research studies, including qualitative, quantitative, mixed methods, and literature reviews published in the English language focused on the use of ChatGPT in nursing education, research, and practice, were included. Dissertations or theses, conference proceedings, government and other organizational reports, white papers, discussion papers, opinion pieces, editorials, commentaries, and published review protocols were excluded. Studies involving other healthcare professionals and/or students without including nursing participants were excluded. Studies exploring other language models without comparison to ChatGPT and those examining the technical specifications of ChatGPT were excluded. Data screening was completed in two stages: titles and abstract and full-text review, followed by data extraction and quality appraisal. Descriptive analysis and narrative synthesis were applied to summarize and categorize the findings. Results: Seventeen studies were included: 15 (88.2 %) focused on nursing education and one each on nursing practice and research. Of the 17 included studies, 5 (29.4 %) were evaluation studies, 3 (17.6 %) were narrative reviews, 3 (17.6 %) were cross-sectional studies, 2 (11.8 %) were descriptive studies, and 1 (5.9 %) was a randomized controlled trial, quasi-experimental study, case study, and qualitative study, respectively. Conclusion: This study has provided a snapshot of ChatGPT usage in nursing education, research, and practice. Although evidence is inconclusive, integration of ChatGPT should consider addressing ethical concerns and ongoing education on ChatGPT usage. Further research, specifically interventional studies, is recommended to ascertain and track the impact of ChatGPT in different contexts.
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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 | 0.028 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".