The DOI matches its Crossref record; the references it cites are not checked yet.
Bridging generational gaps in medication safety: insights from nurses, students, and generative AI models
Open accessAbstract
BACKGROUND: This study investigated medication dose calculation accuracy among nurses, nursing students, and Generative AI (GenAI) models, examining error prevention strategies across generational cohorts. METHODS: A cross-sectional study was conducted from June to August 2024, involving 101 pediatric/neonatal nurses, 91 nursing students, and four GenAI models. Participants completed a questionnaire on calculation proficiency and provided recommendations for error prevention. Qualitative responses were analyzed to describe attitudes and perceptions. RESULTS: 70% of nurses reported previous medication errors compared to 19.5% of students. Thematic analysis identified six key areas for error prevention: double-checking, calculation methods, work environment, training, drug configuration, and technology use. Only students recommended GenAI integration, while nurses emphasized double-checking. CONCLUSIONS: The study highlights generational differences in medication safety approaches and suggests potential benefits of incorporating GenAI as an additional verification layer. These findings contribute to improving nursing education and practice through technological advancements while addressing persistent medication calculation challenges.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI could be used as a verification tool to prevent medication errors.
“Orkaby et al. [13] also suggested that AI could be used as a verification tool to prevent medication errors.”
From Development and pilot evaluation of an AI-based pediatric medication safety virtual simulation program for nursing students: a quasi-experimental study in South Korea (2026) · cites this paper as Orkaby et al. [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
References 17 · Cited by 11
Research works printed but not linked: 3
- Sweller J. Cognitive load theory and educational technology. Edu Technol Res Dev. 2020;68(1):1–16. https://doi.org/10.1007/S11423-019-09701-3/TABLES/1. · DOI
- Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021;21(1):1–23. https://doi.org/10.1186/S12911-021-01488-9/FIGURES/12. · DOI
- Petersson L, Larsson I, Nygren JM, et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Serv Res. 2022;22(1):1–16. https://doi.org/10.1186/S12913-022-08215-8/FIGURES/1. · DOI
Metadata
- Authors
- Orkaby, B., Kerner, E., Saban, M., & Levin, C.
- Affiliations
- Jerusalem College of Technology (IL)
- Tel Aviv University (IL)
- Corresponding author
- Saban, Mor
- Venue
- BMC Nursing
- Published
- 2025-04-07
- Volume
- 24
- Issue
- 1
- Pages
- 382-382
- ISSN
- 1472-6955 (ISSN-L)
- PMC
- PMC11978152
- OpenAlex
- W4409215450
- Cited by
- 11
- References
- 17
- Citations by year
- 2026: 11
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Nursing (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
- Physical Sciences › Engineering › Biomedical Engineering › Intravenous Infusion Technology and Safety
- Health Sciences › Nursing › Research and Theory › Nursing education and management
- Keywords
- drug safety, medication errors, generational differences, nursing students, generative AI, error prevention strategies, double-checking, nursing education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
