Current trends and future implications in the utilization of ChatGPT in nursing: A rapid review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,099 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,022 | 0,022 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,010 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».